Genetic interactions: the missing links for a better understanding of cancer susceptibility, progression and treatment

It is increasingly clear that complex networks of relationships between genes and/or proteins govern neoplastic processes. Our understanding of these networks is expanded by the use of functional genomic and proteomic approaches in addition to computational modeling. Concurrently, whole-genome association scans and mutational screens of cancer genomes identify novel cancer genes. Together, these analyses have vastly increased our knowledge of cancer, in terms of both "part lists" and their functional associations. However, genetic interactions have hitherto only been studied in depth in model organisms and remain largely unknown for human systems. Here, we discuss the importance and potential benefits of identifying genetic interactions at the human genome level for creating a better understanding of cancer susceptibility and progression and developing novel effective anticancer therapies. We examine gene expression profiles in the presence and absence of co-amplification of the 8q24 and 20q13 chromosomal regions in breast tumors to illustrate the molecular consequences and complexity of genetic interactions and their role in tumorigenesis. Finally, we highlight current strategies for targeting tumor dependencies and outline potential matrix screening designs for uncovering molecular vulnerabilities in cancer cells

Predictive and Prognostic Brain Metastases Assessment in Luminal Breast Cancer Patients: FN14 and GRP94 from Diagnosis to Prophylaxis

FN14 has been implicated in many intracellular signaling pathways, and GRP94 is a well-known endoplasmic reticulum protein regulated by glucose. Recently, both have been associated with metastasis progression in breast cancer patients. We studied the usefulness of FN14 and GRP94 expression to stratify breast cancer patients according their risk of brain metastasis (BrM) progression. We analyzed FN14 and GRP94 by immunohistochemistry in a retrospective multicenter study using tissue microarrays from 208 patients with breast carcinomas, of whom 52 had developed BrM. Clinical and pathological characteristics and biomarkers expression in Luminal and non-Luminal patients were analyzed using a multivariate logistic regression model adjusted for covariates, and brain metastasis-free survival (BrMFS) was estimated using the Kaplan–Meier method and the Cox proportional hazards model. FN14 expression was associated with BrM progression mainly in Luminal breast cancer patients with a sensitivity (53.85%) and specificity (89.60%) similar to Her2 expression (46.15 and 89.84%, respectively). Moreover, the likelihood to develop BrM in FN14-positive Luminal carcinomas increased 36.70-fold (3.65–368.25, p = 0.002). Furthermore, the worst prognostic factor for BrMFS in patients with Luminal carcinomas was FN14 overexpression (HR = 8.25; 95% CI: 2.77–24.61; p = 0.00015). In these patients, GRP94 overexpression also increased the risk of BrM (HR = 3.58; 95% CI: 0.98–13.11; p = 0.054—Wald test). Therefore, FN14 expression in Luminal breast carcinomas is a predictive/prognostic biomarker of BrM, which combined with GRP94 predicts BrM progression in non-Luminal tumors 4.04-fold (1.19–8.22, p = 0.025), suggesting that both biomarkers are useful to stratify BrM risk at early diagnosis. We propose a new follow-up protocol for the early prevention of clinical BrM of breast cancer patients with BrM risk

Molecular response to aromatase inhibitor treatment in primary breast cancer

BackgroundAromatase inhibitors such as anastrozole and letrozole are highly effective suppressants of estrogen synthesis in postmenopausal women and are the most effective endocrine treatments for hormone receptor positive breast cancer in such women. Little is known of the molecular effects of these agents on human breast carcinomas in vivo.MethodsWe randomly assigned primary estrogen receptor positive breast cancer patients to treatment with anastrozole or letrozole for 2 weeks before surgery. Expression profiling using cDNA arrays was conducted on pretreatment and post-treatment biopsies. Sample pairs from 34 patients provided sufficient RNA for analysis.ResultsProfound changes in gene expression were seen with both aromatase inhibitors, including many classical estrogen-dependent genes such as TFF1, CCND1, PDZK1 and AGR2, but also many other genes that are likely to represent secondary responses; decrease in the expression of proliferation-related genes were particularly prominent. Many upregulated genes are involved in extracellular matrix remodelling, including collagens and members of the small leucine-rich proteoglycan family (LUM, DCN, and ASPN). No significant differences were seen between letrozole and anastrozole in terms of molecular effects. The gene changes were integrated into a Global Index of Dependence on Estrogen (GIDE), which enumerates the genes changing by at least twofold with therapy. The GIDE varied markedly between tumours and related significantly to pretreatment levels of HER2 and changes in immunohistochemically detected Ki67.ConclusionOur findings identify the transcriptional signatures associated with aromatase inhibitor treatment of primary breast tumours. Larger datasets using this approach should enable identification of estrogen-dependent molecular changes, which are the determinants of benefit or resistance to endocrine therapy

A novel inhibitor of fatty acid synthase shows activity against HER2+ breast cancer xenografts and is active in anti-HER2 drug-resistant cell lines

Introduction: Inhibiting the enzyme Fatty Acid Synthase (FASN) leads to apoptosis of breast carcinoma cells, and this is linked to human epidermal growth factor receptor 2 (HER2) signaling pathways in models of simultaneous expression of FASN and HER2. Methods: In a xenograft model of breast carcinoma cells that are FASN+ and HER2+, we have characterised the anticancer activity and the toxicity profile of G28UCM, the lead compound of a novel family of synthetic FASN inhibitors. In vitro, we analysed the cellular and molecular interactions of combining G28UCM with anti-HER drugs. Finally, we tested the cytotoxic ability of G28UCM on breast cancer cells resistant to trastuzumab or lapatinib, that we developed in our laboratory. Results: In vivo, G28UCM reduced the size of 5 out of 14 established xenografts. In the responding tumours, we observed inhibition of FASN activity, cleavage of poly-ADPribose polymerase (PARP) and a decrease of p-HER2, p- protein kinase B (AKT) and p-ERK1/2, which were not observed in the nonresponding tumours. In the G28UCM-treated animals, no significant toxicities occurred, and weight loss was not observed. In vitro, G28UCM showed marked synergistic interactions with trastuzumab, lapatinib, erlotinib or gefitinib (but not with cetuximab), which correlated with increases in apoptosis and with decreases in the activation of HER2, extracellular signal-regulated kinase (ERK)1/2 and AKT. In trastuzumab-resistant and in lapatinib-resistant breast cancer cells, in which trastuzumab and lapatinib were not effective, G28UCM retained the anticancer activity observed in the parental cells. Conclusions: G28UCM inhibits fatty acid synthase (FASN) activity and the growth of breast carcinoma xenografts in vivo, and is active in cells with acquired resistance to anti-HER2 drugs, which make it a candidate for further pre-clinical development

Biological Convergence of Cancer Signatures

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties

Predictive and Prognostic Brain Metastases Assessment in Luminal Breast Cancer Patients: FN14 and GRP94 from Diagnosis to Prophylaxis

FN14 has been implicated in many intracellular signaling pathways, and GRP94 is a well-known endoplasmic reticulum protein regulated by glucose. Recently, both have been associated with metastasis progression in breast cancer patients. We studied the usefulness of FN14 and GRP94 expression to stratify breast cancer patients according their risk of brain metastasis (BrM) progression. We analyzed FN14 and GRP94 by immunohistochemistry in a retrospective multicenter study using tissue microarrays from 208 patients with breast carcinomas, of whom 52 had developed BrM. Clinical and pathological characteristics and biomarkers expression in Luminal and non-Luminal patients were analyzed using a multivariate logistic regression model adjusted for covariates, and brain metastasis-free survival (BrMFS) was estimated using the Kaplan–Meier method and the Cox proportional hazards model. FN14 expression was associated with BrM progression mainly in Luminal breast cancer patients with a sensitivity (53.85%) and specificity (89.60%) similar to Her2 expression (46.15 and 89.84%, respectively). Moreover, the likelihood to develop BrM in FN14-positive Luminal carcinomas increased 36.70-fold (3.65–368.25, p = 0.002). Furthermore, the worst prognostic factor for BrMFS in patients with Luminal carcinomas was FN14 overexpression (HR = 8.25; 95% CI: 2.77–24.61; p = 0.00015). In these patients, GRP94 overexpression also increased the risk of BrM (HR = 3.58; 95% CI: 0.98–13.11; p = 0.054—Wald test). Therefore, FN14 expression in Luminal breast carcinomas is a predictive/prognostic biomarker of BrM, which combined with GRP94 predicts BrM progression in non-Luminal tumors 4.04-fold (1.19–8.22, p = 0.025), suggesting that both biomarkers are useful to stratify BrM risk at early diagnosis. We propose a new follow-up protocol for the early prevention of clinical BrM of breast cancer patients with BrM risk

Biological convergence of cancer signatures

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties