Cancer Research in Biotechnology Part II – Genetic Analysis

Gene expression profiling (GEP) is a powerful tool to identify genes and pathways that are abnormally expressed during carcinogenesis. While these discoveries enhance our understanding of molecular pathogenesis, they can also suggest novel therapeutic targets, provide information about drug resistance pathways, and refine prognostic classifications.

A major problem in clinical oncology is the heterogeneous response of histologically similar tumors to treatments such as cytotoxic chemotherapy. With the exception of estrogen/progesterone receptor, and HER2 (c-erbB-2) expression in breast cancer (for hormonal therapy and trastuzumab, respectively), EGFR kinase domain mutations and genomic amplification in lung cancer (in EGFR targeted inhibitors gefitinib or erlotinib), and K-ras mutations in colon cancer (lack of response to EGFR-targeted antagonists), there are no other single molecules that are clinically useful predictors of response validated for any form of anticancer therapy.

Encouraging emerging data suggest that prediction of response to chemotherapy or biologically targeted agents may be possible by analyzing gene expression profiles (GEPs). With rapid advances in the DNA microarray technologies and more sophisticated studies, microarray analysis has started to make ways into clinical trials and practices in oncology.

Several examples of the potential application of GEPs in clinical oncology are described here to illustrate the utility of this technology in common solid tumors and hematologic malignancies.

Acute leukemia – The first report to show that GEPs could be used to classify tumors analyzed a group of acute leukemias. Based upon gene expression patterns, acute myeloid leukemia (AML) could be distinguished from acute lymphoblastic leukemia (ALL) without standard histology information. Similarly, B-cell versus T-cell ALL could be separated based on GEP. This study served as proof of principle that clinically useful classifications could be made simply by gene expression patterns. In another report, a case with equivocal histology by standard criteria was accurately classified by gene analysis, demonstrating the potential utility of GEP beyond standard histologic and immunocytochemical methods.

Others have shown that GEP can distinguish among prognostically important subgroups of children with ALL and adults with AML, in some cases identifying those who eventually fail therapy. If these findings are confirmed by others, the logical next step is to apply a more intense initial treatment strategy to such patients, selected on the basis of their GEP.

It may also be possible to screen for agents capable of inducing leukemic cell differentiation through changes in their GEP. A major caveat is that gene expression profiles of clinical samples may differ significantly from those seen in cell lines representing the corresponding leukemia.

Prostate cancer – A potential application of GEP in men at risk for prostate cancer is the identification of biomarkers that can help select men with a borderline elevation in serum prostate specific antigen (PSA) for biopsy. In addition, GEP might be used to identify men whose early stage tumors are destined to recur and thus would benefit from more aggressive therapy.

GEP has been used to identify several genes (e.g., hepsin and pim-1) that are upregulated in prostate cancer compared to benign prostatic hyperplasia and normal prostate tissue, and some are highly correlated with clinical outcome [25-30]. Investigators have found a ≥3-fold difference in expression in over 3000 genes when nonrecurrent prostate cancers were compared to metastatic tumors.

Colon cancer – The serine phosphatase PRL-3 is consistently upregulated in metastatic as compared to non-metastatic colorectal cancers [31]. The finding that metastatic potential appears to be encoded in the primary has challenged the notion that metastases arise from rare cells that have acquired the ability to metastasize.

GEP is under study as a way to improve prognostication, and perhaps, individualize adjuvant therapy recommendations. An area of intense study is the use of GEP to predict which patients with node-negative resected colon cancer are at a relatively higher risk of relapse, and thus, might benefit from adjuvant chemotherapy, as is typically recommended for patients with node-positive disease.

Breast cancer – A molecular classification for breast cancer has been proposed based upon GEP. Luminal (mainly estrogen receptor [ER] positive), basal-like (mostly ER-negative), normal-like, and erbB2+ (mostly HER-2 overexpressing, ER-negative) subgroups have been identified, and have different prognoses.

A major area of investigation is the use of such molecular profiling to predict response to therapy. The GEPs of breast cancers that respond best to neoadjuvant (preoperative) chemotherapy (ie, basal-like, erbB2+) differ from those of nonresponding or resistant tumors.

Analysis of GEP can also distinguish sporadic breast cancers from those associated with BRCA mutations. Perhaps more importantly, GEP can also permit stratification of defined subgroups (ie, those with axillary lymph node-negative breast cancer or grade 2 tumors) into prognostically separate categories. In at least some reports, outcome prediction by GEP outperforms existing prognostic classifications. This topic is discussed in detail elsewhere.

GEP analysis by DNA microarray is available in patients with breast cancer (the 21-gene recurrence score assay, like Oncotype DX) to quantify the likelihood of a breast cancer recurrence in women with newly diagnosed, node-negative hormone receptor-positive early stage breast cancer. The assay is designed to identify those women whose risk of recurrence is low enough to justify the omission of chemotherapy and use of tamoxifen alone as systemic adjuvant therapy.

Although commercially available, the benefit of using 21-gene recurrence score assays (e.g, Oncotype DX) to select the adjuvant therapy strategy has not been tested in a prospective trial. Such an approach is being evaluated in the phase III Trial Assigning IndividuaLized Options for Treatment (Rx) (the TAILORx clinical trial), sponsored by the National Cancer Institute and led by the Eastern Cooperative Oncology Group (ECOG).