An Integrated Genomic-Based Approach to Individualized Treatment of Patients With Advanced-Stage Ovarian Cancer

Novel agents in ovarian cancer

The standard treatment for epithelial ovarian cancer remains surgical debulking and chemotherapy with carboplatin and paclitaxel. However, the majority of patients relapse, and few, if any, achieve a cure. Future advancement in treatment should aim at targeting the biology of the disease, specifically mechanisms critical to tumor initiation and progression. Several Phase I and II clinical trials have identified novel opportunities for therapy. The most promising venues appear to be the antiangiogenic agents and the inhibitors of intracellular signaling. Novel modalities of delivering cytotoxics to tumor cells by exploiting ovarian cancer-specific biomarkers are also being tested, and appear promising. Immunomodulatory agents are being developed for consolidation therapy. Although devoid of the common side effects associated with chemotherapy, the use of targeted agents is associated with specific toxicities, related to the biological processes they block. The main challenge for future successful clinical development will be defining molecular markers predictive of response and judicious patient selection based on the biological features of the tumor. Individualized treatment driven by molecular characteristics will open the door to a new age in anticancer medicine.

A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery

The U.S. National Cancer Institute has used a panel of 60 diverse human cancer cell lines (the NCI-60) to screen >100,000 chemical compounds for anticancer activity. However, not all important cancer types are included in the panel, nor are drug responses of the panel predictive of clinical efficacy in patients. We asked, therefore, whether it would be possible to extrapolate from that rich database (or analogous ones from other drug screens) to predict activity in cell types not included or, for that matter, clinical responses in patients with tumors. We address that challenge by developing and applying an algorithm we term "coexpression extrapolation" (COXEN). COXEN uses expression microarray data as a Rosetta Stone for translating from drug activities in the NCI-60 to drug activities in any other cell panel or set of clinical tumors. Here, we show that COXEN can accurately predict drug sensitivity of bladder cancer cell lines and clinical responses of breast cancer patients treated with commonly used chemotherapeutic drugs. Furthermore, we used COXEN for in silico screening of 45,545 compounds and identify an agent with activity against human bladder cancer.

Mining gene expression profiles: expression signatures as cancer phenotypes

Many examples highlight the power of gene expression profiles, or signatures, to inform an understanding of biological phenotypes. This is perhaps best seen in the context of cancer, where expression signatures have tremendous power to identify new subtypes and to predict clinical outcomes. Although the ability to interpret the meaning of the individual genes in these signatures remains a challenge, this does not diminish the power of the signature to characterize biological states. The use of these signatures as surrogate phenotypes has been particularly important, linking diverse experimental systems that dissect the complexity of biological systems with the in vivo setting in a way that was not previously feasible.