Transcriptomic characterization of the histopathological growth patterns in breast cancer liver metastases.

Metastatic breast cancer (mBC) remains incurable and liver metastases (LM) are observed in approximately 50% of all patients with mBC. In some cases, surgical resection of breast cancer liver metastases (BCLM) is associated with prolonged survival. However, there are currently no validated marker to identify these patients. The interactions between the metastatic cancer cells and the liver microenvironment result in two main histopathological growth patterns (HGP): replacement (r-HGP), characterized by a direct contact between the cancer cells and the hepatocytes, and desmoplastic (d-HGP), in which a fibrous rim surrounds the tumor cells. In patients who underwent resection of BCLM, the r-HGP is associated with a worse postoperative prognosis than the d-HGP. Here, we aim at unraveling the biological differences between these HGP within ten patients presenting both HGP within the same metastasis. The transcriptomic analyses reveal overexpression of genes involved in cell cycle, DNA repair, vessel co-option and cell motility in r-HGP while angiogenesis, wound healing, and several immune processes were found overexpressed in d-HGP LM. Understanding the biology of the LM could open avenues to refine treatment of BC patients with LM.

Rapid autopsies to enhance metastatic research: the UPTIDER post-mortem tissue donation program.

Research on metastatic cancer has been hampered by limited sample availability. Here we present the breast cancer post-mortem tissue donation program UPTIDER and show how it enabled sampling of a median of 31 (range: 5-90) metastases and 5-8 liquids per patient from its first 20 patients. In a dedicated experiment, we show the mild impact of increasing time after death on RNA quality, transcriptional profiles and immunohistochemical staining in tumor tissue samples. We show that this impact can be counteracted by organ cooling. We successfully generated ex vivo models from tissue and liquid biopsies from distinct histological subtypes of breast cancer. We anticipate these and future findings of UPTIDER to elucidate mechanisms of disease progression and treatment resistance and to provide tools for the exploration of precision medicine strategies in the metastatic setting.

Molecular Subtyping Combined with Biological Pathway Analyses to Study Regorafenib Response in Clinically Relevant Mouse Models of Colorectal Cancer.

PURPOSE: Regorafenib (REG) is approved for the treatment of metastatic colorectal cancer, but has modest survival benefit and associated toxicities. Robust predictive/early response biomarkers to aid patient stratification are outstanding. We have exploited biological pathway analyses in a patient-derived xenograft (PDX) trial to study REG response mechanisms and elucidate putative biomarkers. EXPERIMENTAL DESIGN: Molecularly subtyped PDXs were annotated for REG response. Subtyping was based on gene expression (CMS, consensus molecular subtype) and copy-number alteration (CNA). Baseline tumor vascularization, apoptosis, and proliferation signatures were studied to identify predictive biomarkers within subtypes. Phospho-proteomic analysis was used to identify novel classifiers. Supervised RNA sequencing analysis was performed on PDXs that progressed, or did not progress, following REG treatment. RESULTS: Improved REG response was observed in CMS4, although intra-subtype response was variable. Tumor vascularity did not correlate with outcome. In CMS4 tumors, reduced proliferation and higher sensitivity to apoptosis at baseline correlated with response. Reverse phase protein array (RPPA) analysis revealed 4 phospho-proteomic clusters, one of which was enriched with non-progressor models. A classification decision tree trained on RPPA- and CMS-based assignments discriminated non-progressors from progressors with 92% overall accuracy (97% sensitivity, 67% specificity). Supervised RNA sequencing revealed that higher basal EPHA2 expression is associated with REG resistance. CONCLUSIONS: Subtype classification systems represent canonical "termini a quo" (starting points) to support REG biomarker identification, and provide a platform to identify resistance mechanisms and novel contexts of vulnerability. Incorporating functional characterization of biological systems may optimize the biomarker identification process for multitargeted kinase inhibitors.