Metabolic classification of non-small cell lung cancer patient-derived xenografts by a digital pathology approach: A pilot study.

Introduction: Genetically characterized patient-derived tumor xenografts (PDX) are a valuable resource to understand the biological complexity of cancer and to investigate new therapeutic approaches. Previous studies, however, lack information about metabolic features of PDXs, which may limit testing of metabolism targeting drugs. Methods: In this pilot study, we investigated by immunohistochemistry (IHC) expression of five essential metabolism-associated markers in a set of lung adenocarcinoma PDX samples previously established and characterized. We exploited digital pathology to quantify expression of the markers and correlated results with tumor cell proliferation, angiogenesis and time of PDX growth in mice. Results: Our results indicate that the majority of the analyzed PDX models rely on oxidative phosphorylation (OXPHOS) metabolism, either alone or in combination with glucose metabolism. Double IHC enabled us to describe spatial expression of the glycolysis-associated monocarboxylate transporter 4 (MCT4) marker and the OXPHOS-associated glutaminase (GLS) marker. GLS expression was associated with cell proliferation and with expression of liver-kinase B1 (LKB1), a tumor suppressor involved in the regulation of multiple metabolic pathways. Acetyl CoA carboxylase (ACC) was associated with the kinetics of PDX growth. Conclusion: Albeit limited by the small number of samples and markers analyzed, metabolic classification of existing collections of PDX by this mini panel will be useful to inform pre-clinical testing of metabolism-targeting drugs.

Quantification of Tumor and Angiogenesis-Related Markers in Ovarian Cancer Models by a Digital Pathology Approach.

Digital pathology has the potential to quantify tumor markers accurately and reproducibly with various cellular and subcellular localizations in tissues, thus filling a need in cancer research. As a case study, we quantified the percentage of necrosis, microvessels density, and monocarboxylate transporter 4 (MCT4) expression in two ovarian cancer patient-derived xenograft (PDX) models subcutaneously injected in NOD/SCID mice. PDX models were treated with bevacizumab, an antiangiogenic drug, that targets vascular endothelial growth factor A (VEGF-A). Specific signal analysis algorithms allowed us to study morphologic, vascular, and metabolic modifications induced by antiangiogenic therapy by a quantitative, reproducible, and reliable approach.

In situ Metabolic Profiling of Ovarian Cancer Tumor Xenografts: A Digital Pathology Approach.

Metabolic profiling of cancer is a rising interest in the field of biomarker development. One bottleneck of its clinical exploitation, however, is the lack of simple and quantitative techniques that enable to capture the key metabolic traits of tumor from archival samples. In fact, liquid chromatography associated with mass spectrometry is the gold-standard technique for the study of tumor metabolism because it has high levels of accuracy and precision. However, it requires freshly frozen samples, which are difficult to collect in large multi-centric clinical studies. For this reason, we propose here to investigate a set of established metabolism-associated protein markers by exploiting immunohistochemistry coupled with digital pathology. As case study, we quantified expression of MCT1, MCT4, GLS, PHGDH, FAS, and ACC in 17 patient-derived ovarian cancer xenografts and correlated it with survival. Among these markers, the glycolysis-associated marker MCT4 was negatively associated with survival of mice. The algorithm enabling a quantitative analysis of these metabolism-associated markers is an innovative research tool that can be exported to large sets of clinical samples and can remove the variability of individual interpretation of immunohistochemistry results.

BRAF Gene Copy Number and Mutant Allele Frequency Correlate with Time to Progression in Metastatic Melanoma Patients Treated with MAPK Inhibitors.

Metastatic melanoma is characterized by complex genomic alterations, including a high rate of mutations in driver genes and widespread deletions and amplifications encompassing various chromosome regions. Among them, chromosome 7 is frequently gained in BRAF-mutant melanoma, inducing a mutant allele-specific imbalance. Although BRAF amplification is a known mechanism of acquired resistance to therapy with MAPK inhibitors, it is still unclear if BRAF copy-number variation and BRAF mutant allele imbalance at baseline can be associated with response to treatment. In this study, we used a multimodal approach to assess BRAF copy number and mutant allele frequency in pretreatment melanoma samples from 46 patients who received MAPK inhibitor-based therapy, and we analyzed the association with progression-free survival. We found that 65% patients displayed BRAF gains, often supported by chromosome 7 polysomy. In addition, we observed that 64% patients had a balanced BRAF-mutant/wild-type allele ratio, whereas 14% and 23% patients had low and high BRAF mutant allele frequency, respectively. Notably, a significantly higher risk of progression was observed in patients with a diploid BRAF status versus those with BRAF gains [HR, 2.86; 95% confidence interval (CI), 1.29-6.35; P = 0.01] and in patients with low percentage versus those with a balanced BRAF mutant allele percentage (HR, 4.54; 95% CI, 1.33-15.53; P = 0.016). Our data suggest that quantitative analysis of the BRAF gene could be useful to select the melanoma patients who are most likely to benefit from therapy with MAPK inhibitors. Mol Cancer Ther; 17(6); 1332-40. ©2018 AACR.