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Latest in Literature

Review of the most recent evidence-based literature discussing breast, colorectal, lung, and ovarian cancers, in addition to melanoma and multiple myeloma.

Where Are We Spending the Most Money in Metastatic Lung Cancer?

Metastatic lung cancer is frequently diagnosed and extremely difficult to treat; a great deal of resources are expended on therapies and health services for patients with metastatic lung cancer once they undergo chemotherapy. In 2004, lung cancer treatment expenditures were one-fifth of Medicare’s entire spending on cancer. The benefits of lung cancer treatment are limited, and therefore therapy costs must be weighed against outcomes. This is particularly true today, as there is increased scrutiny on the costs of care—even cancer care.

Researchers from Amgen, Kaiser Permanente, and the health economic consultant Policy Analysis Inc studied the health claims of 4068 patients (mean age, 65 years) who began chemotherapy between 2000 and 2006 until their deaths or the end of the study period to determine cumulative healthcare resource utilization and expenditures. The information was collected from a large, private, multi-payer health insurance claims database. Patients’ inpatient and outpatient service claims and medication claims were followed for a median of 334 days.

Over a mean 500-day follow-up period, the cumulative healthcare expenditures averaged $125,849 per patient. Total healthcare costs included 34% for outpatient care, such as emergency department and/or physician office visits, hospital outpatient/home health/hospice/skilled nursing services, and 20% for acute hospital inpatient care. Outpatient chemotherapy and other medication costs were estimated at 22% and 24%, respectively (Figure). For patients whose total costs were above $200,000, chemotherapy and other medication costs accounted for more than 50% of the total, percentages similar to those with far lower total costs.

The majority of costs for patients with metastatic lung cancer receiving chemotherapy are associated with outpatient and not inpatient care, the researchers pointed out. This may be important, they added, when allocating overall healthcare resources, identifying possible cost savings from disease prevention, and assessing the cost-effectiveness of new medical interventions. Cost-effectiveness evaluations of new strategies for preventing, screening, and treating early stage and metastatic lung cancer can be useful when regulatory and reimbursement decisions must be made, according to the researchers.

Source: Vera-Llonch M, Weycker D, Glass A, et al. Healthcare costs in patients with metastatic lung cancer receiving chemotherapy. BMC Health Services Research. 2011;11:305.


Treatment Response and Survival in Patients With Epithelial Ovarian Cancer

Interest in the use of biomarkers has grown to help determine which patients have a greater likelihood of receiving optimal benefit from oncologic treatments. This concept of optimizing the value of treatment is now being applied to another aspect of cancer care. Investigators from the University of Minnesota used mathematical modeling to help determine whether global gene expression data of tumor tissue attained during surgery and before the beginning of chemotherapy treatment in 54 patients with advanced stage epithelial ovarian cancer (EOC) can help predict which patients would mostly likely suffer a recurrence.

The researchers developed 3 prognostic biomarker models (termed F1, F2, and F3) based on the existence of tumor gene combinations (from a total of 12 genes) that were very accurate (overall sensitivity: 96%–100% and overall specificity: 96%–100%) in identifying both the patients who responded (long-term survivors [LTS]) and the non-responders (short-term survivors [STS]) to platinum/taxol chemotherapy. From a total population of 54 individuals, the researchers randomly selected 34 patients (14 LTS, 20 STS) in order to develop and test the F1, F2, and F3 prognostic biomarker models. This discovery study phase was the first step in assessing a prognostic (or a diagnostic) test, resulting in extremely high sensitivity and specificity. In the validation study, information from the tumors of 20 patients (not included in the discovery phase) were used to test the 3 prognostic biomarker models. In this phase, all 3 models gave a correct prognosis for the 20 unknown subjects with regard to treatment response and survival.

When the discovery study and validation study results were then combined, the F1 and F3 models had a treatment response sensitivity of 100% (24/24 LTS subjects) and a specificity of 97% (29/30 STS subjects). The F2 model demonstrated a sensitivity of 96% (23/24 LTS subjects) and a specificity of 1.000 (30/30 STS subjects). In terms of survival, both the F1 and F3 models demonstrated a sensitivity of 97% (29/30 STS subjects) and a specificity of 100% (24/24 subjects), whereas the F2 model had perfect sensitivity (30/30 STS subjects) and a specificity of 96% (23/24 subjects).

The 12 tumor genes tested were categorized into 3 general groups: (1) genes that regulate cytostructural protein expression, (2) genes for cell proliferation regulation, and (3) metabolism regulation genes.

By using these prognostic biomarker tests to develop new, more effective pharmacological regimens, the researchers noted, considerably more successful treatments and a significant increase in survival rates for all patients with advanced stage EOC are possible.

Source: Nikas JB, Boylan KLM, Skubitz APM, Low WC. Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer. Cancer Informatics. 2011:10;233-247.


A “Poster Child” for Targeted Cancer Therapy?

Multiple myeloma (MM) exhibits considerable biologic and clinical variation—it is not surprising, then, that a single, uniform therapeutic approach for patients with MM fails more often than it succeeds, said commentators from the University of Texas MD Anderson Cancer Center, Houston, and Changzheng Hospital, Shanghai, China.

Much work had gone into developing a 70-gene and 15-gene model, created by the University of Arkansas for Medical Sciences (UAMS) and the Intergroupe Francophone du Myelome, respectively, to identify patient populations who are at risk of poor outcomes as determinedby their tumor gene expression profiles (GEPs). Effective as well as distinct treatments for different MM subtype patients that improved response rate, progression-free survival, and overall survival with low toxicity and rational cost-effectiveness was the ultimate aim of the stratification.

Total therapy 4 (TT4) by UAMS and Mayo Stratification of Myeloma Risk-Adapted Therapy consensus guidelines from the Mayo Clinic are 2 typical examples of targeted therapy, based on the GEP stratifications. A 2-year complete response will be the primary clinical end point of ongoing research to determine the validity of guidelines. Mayo Clinic researchers categorized patients into standard-risk, intermediate-risk, and high-risk groups depending on mutations and abnormalities discovered through multiple methods. Sixty percent were standard risk, and 20% of patients were determined to be in the high- and low-risk groups. Personalized therapy recommendations included regimens that could result in a high overall response rate with minimal early toxicity for a standard-risk patient. Those in the intermediate- risk group could follow a bortezomib- based induction regimen (high-dose melphalan) with or without consolidation before lenalidomide maintenance. The authors pointed out that in the high-risk subjects, new drugs and novel approaches may be best, and a “total therapy 3 like” approach may be most appropriate for patients exhibiting p53 deletion.

Although there are now over 40 novel pharmacologic targets presently undergoing clinical trials, the authors acknowledged that it is still not clear which will play a critical role for any subgroup of patients with multiple myeloma. Strengthening the insights on patient stratifications according to molecular heterogeneity and making rationally cost-effective treatment available for different subsets of patients should be the goal, they noted, and can be achieved by optimizing the current therapeutic strategies. A particular emphasis should be placed on immunomodulatory therapies, the authors concluded, because of their potential to eliminate remaining myeloma cells that will enhance the efficacy after a bonemarrow transplant.

Source: Jiang H, Yi Q, Hou J. Strategic consideration on treatment of multiple myeloma (editorial). Chin Med J. 2011;124:2965-2968.


Developments in Treatment Approaches to Patients With HER-2+ Breast Cancer Refractory to Trastuzumab

Before the introduction of trastuzumab in the 1990s, women with breast tumors overexpressing the human epidermal growth factor receptor (HER)-2 gene had significantly shorter survival than patients found to be negative for HER-2 overexpression. The advent of targeted therapy with trastuzumab was touted as one of the first successes in matching molecular biomarkers with effective pharmacologic treatment. Overall survival in patients receiving this monoclonal antibody in addition to chemotherapy had an overall survival that was 5 months longer than those who did not receive the combination as first-line therapy. However, more than 50% of women with advanced breast cancer exhibit resistance to trastuzumab, and disease progression and/or recurrence is common in those who originally respond to therapy. Oncologists from China and the United Kingdom sought to review the mechanisms of resistance and find new ways of classifying molecular pathways that would identify biomarkers, enabling additional methods for effectively stratifying patients who will benefit more fully from trastuzumab treatment.

They categorized the possible causes for resistance to trastuzumab as follows:

1. Expression of p95HER-2, an abbreviated form of HER-2 that does not possess the binding receptor to which trastuzumab attaches

2. Activation of the PI3K-AKT pathway (in a small retrospective study, patients demonstrating activation of this pathway had poor response to treatment)

3. Abnormal signaling of other receptors, such as HER-3, insulin-like growth factor 1 receptor, and EGFR

4. Other miscellaneous mechanisms, such as expression of glycoprotein mucin 4 and autocrine production of transforming growth factor-beta

Much work is ongoing to find medications that are useful in patients with trastuzumab resistance, which work against 1 or more of these pathways. For example, lapatinib’s response rates as monotherapy are very low (up to around 5%). Response rates in combination with other chemotherapy does exhibit differences in time to disease progression of about 2 months. Multi-HER tyrosine-kinase inhibitors are currently in clinical trials that show some promise in trastuzumabresistant patients, helping about one-fourth of patients in a phase 2 trial.

The authors also pointed out that more research is needed to clarify the molecular mechanisms differentiating trastuzumab-resistant and –refractory breast cancer. It may also be that this intensive research reveals that resistance may be less frequent with another monoclonal antibody related to trastuzumab, like neratinib, and that this should be used eventually as first-line therapy.

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