Ariel Anguiano

An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer.

Background A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patients probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. Methods and Results Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. Conclusions Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities.

Gene Expression Profiles of Tumor Biology Provide a Novel Approach to Prognosis and May Guide the Selection of Therapeutic Targets in Multiple Myeloma

PURPOSE Monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) comprise heterogeneous disorders with incompletely understood molecular defects and variable clinical features. We performed gene expression profiling (GEP) with microarray data to better dissect the molecular phenotypes, sensitivity to particular chemotherapeutic agents, and prognoses of these diseases. METHODS Using gene expression and clinical data from 877 patients ranging from normal plasma cells (NPC) to relapsed MM (RMM), we applied gene expression signatures reflecting deregulation of oncogenic pathways and tumor microenvironment to highlight molecular changes that occur as NPCs transition to MM, create a high-risk MGUS gene signature, and subgroup International Staging System (ISS) stages into more prognostically accurate clusters of patients. Lastly, we used gene signatures to predict sensitivity to conventional cytotoxic chemotherapies among identified clusters of patients. RESULTS Myc upregulation and increasing chromosomal instability (CIN) characterized the evolution from NPC to RMM (P < .0001 for both). Studies of MGUS revealed that some samples shared biologic features with RMM, which comprised the basis for a high-risk MGUS signature. Regarding MM, we subclassified ISS stages into clusters based on shared features of tumor biology. These clusters differentiated themselves based on predictions for prognosis and chemotherapy sensitivity (eg, in ISS stage I, one cluster was characterized by increased CIN, cyclophosphamide resistance, and a poor prognosis). CONCLUSION GEP provides insight into the molecular defects underlying plasma cell dyscrasias that may explain their clinical heterogeneity. GEP also may also refine current prognostic and therapeutic models for MGUS and MM.

Toward the individualization of lung cancer therapy

The goal of personalized cancer medicine is to effectively match the right treatment strategy with the patient. This includes an improvement of prognosis to identify which patients should be treated as well as the ability to predict which therapy will be most effective for the individual patient. Recent advances in the use of genomic technologies, primarily gene expression profiling with DNA microarrays, has provided mechanisms to address each of these questions. The prognosis of early‐stage lung cancer patients (stage IA) is clearly imprecise because nearly 30% of these patients will develop disease recurrence (determined according to the TNM staging system). Gene expression profiles have been developed that can accurately predict recurrence and, when applied to the population of patients with stage IA disease, demonstrate a capacity to potentially identify those individuals likely to have been misclassified as low risk. In addition, gene expression information has also demonstrated an ability to predict who will respond to a particular chemotherapy regimen, providing a further opportunity to more effectively guide the selection of available therapies that best match the individual patient. Finally, other strategies offer the hope of better using the newly developed, experimental therapies that target specific components of the oncogenic process. Taken together, these new genomic tools provide the opportunity to develop rational strategies for treating the individual lung cancer patient. Cancer 2008;113(7 suppl):1760–7.

Genomic signatures individualize therapeutic decisions in non-small-cell lung cancer

Gene expression signatures have been developed in an effort to dissect the biologic phenotypes of malignancies. These signatures have tremendous power to identify new cancer subtypes and to predict clinical outcomes based on patterns of gene expression. Expression profiles specific to a phenotype can be derived from in vitro data, as well as from patient cohorts with clinically relevant outcomes. In addition to predicting outcomes in non-small-cell lung cancer (NSCLC), similar techniques have been used to develop gene expression signatures that predict sensitivity or resistance to specific chemotherapeutic agents. Additionally, expression data have been used to identify oncogenic pathway deregulation to help direct the use of targeted agents. Used in combination, it is likely that gene expression signatures will help assess prognosis and may also be of value in guiding the use of cytotoxic and targeted therapy in NSCLC. Clinical trials are ongoing to validate these predictive gene expression signatures in a prospective manner.

Retraction: Acharya CR, et al. Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer. JAMA. 2008;299(13):1574-1587.

To the Editor: We would like to retract the article entitled “Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer,” which was published in the April 2, 2008, issue of JAMA. A component of this article reported the use of chemotherapy sensitivity predictions based on an approach described by Potti et al in Nature Medicine in 2006. The Nature Medicine article was recently retracted due to an inability to reproduce the results with the chemotherapy signatures. Because a significant component of this JAMA article was based on the use of chemotherapy signatures reported in the Nature Medicine paper, we have decided to retract the JAMA article. We apologize for any negative impact on scientific research or clinical care caused by the publication of our article in JAMA.