A Multigene Prognostic Assay for Selection of Adjuvant Chemotherapy in Patients with T3, Stage II Colon Cancer: Impact on Quality-Adjusted Life Expectancy and Costs
ABSTRACT Uncertainty exists regarding appropriate and affordable use of adjuvant chemotherapy in stage II colon cancer (T3, proficient DNA mismatch repair). This study aimed to estimate the effectiveness and costs from a US societal perspective of a multigene recurrence score (RS) assay for patients recently diagnosed with stage II colon cancer (T3, proficient DNA mismatch repair) eligible for adjuvant chemotherapy.
RS was compared with guideline-recommended clinicopathological factors (tumor stage, lymph nodes examined, tumor grade, and lymphovascular invasion) by using a state-transition (Markov) lifetime model. Data were obtained from published literature, a randomized controlled trial (QUick And Simple And Reliable) of adjuvant chemotherapy, and rates of chemotherapy use from the National Cooperative Cancer Network Colon/Rectum Cancer Outcomes study. Life-years, quality-adjusted life expectancy, and lifetime costs were examined.
The RS is projected to reduce adjuvant chemotherapy use by 17% compared with current treatment patterns and to increase quality-adjusted life expectancy by an average of 0.035 years. Direct medical costs are expected to decrease by an average of $2971 per patient. The assay was cost saving for all subgroups of patients stratified by clinicopathologic factors. The most influential variables affecting treatment decisions were projected years of life remaining, recurrence score, and patients' disutilities associated with adjuvant chemotherapy.
Use of the multigene RS to assess recurrence risk after surgery in stage II colon cancer (T3, proficient DNA mismatch repair) may reduce the use of adjuvant chemotherapy without decreasing quality-adjusted life expectancy and be cost saving from a societal perspective. These findings need to be validated in additional cohorts, including studies of clinical practice as assay use diffuses into nonacademic settings.
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ABSTRACT: The burden of cancer is growing, and the disease is becoming a major economic expenditure for all developed countries. In 2008, the worldwide cost of cancer due to premature death and disability (not including direct medical costs) was estimated to be US$895 billion. This is not simply due to an increase in absolute numbers, but also the rate of increase of expenditure on cancer. What are the drivers and solutions to the so-called cancer-cost curve in developed countries? How are we going to afford to deliver high quality and equitable care? Here, expert opinion from health-care professionals, policy makers, and cancer survivors has been gathered to address the barriers and solutions to delivering affordable cancer care. Although many of the drivers and themes are specific to a particular field-eg, the huge development costs for cancer medicines-there is strong concordance running through each contribution. Several drivers of cost, such as over-use, rapid expansion, and shortening life cycles of cancer technologies (such as medicines and imaging modalities), and the lack of suitable clinical research and integrated health economic studies, have converged with more defensive medical practice, a less informed regulatory system, a lack of evidence-based sociopolitical debate, and a declining degree of fairness for all patients with cancer. Urgent solutions range from re-engineering of the macroeconomic basis of cancer costs (eg, value-based approaches to bend the cost curve and allow cost-saving technologies), greater education of policy makers, and an informed and transparent regulatory system. A radical shift in cancer policy is also required. Political toleration of unfairness in access to affordable cancer treatment is unacceptable. The cancer profession and industry should take responsibility and not accept a substandard evidence base and an ethos of very small benefit at whatever cost; rather, we need delivery of fair prices and real value from new technologies.The Lancet Oncology 09/2011; 12(10):933-80. DOI:10.1016/S1470-2045(11)70141-3 · 24.73 Impact Factor
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ABSTRACT: Background: Only a minority of prostate cancer patients with adverse pathology and biochemical recurrence (BCR) post radical prostatectomy (RP) experience metastasis and die from prostate cancer. Improved risk prediction models using genomic information may enable clinicians to better weigh the risk of metastasis and the morbidity and costs of treatment in a clinically heterogeneous population. Purpose: We present a clinical utility study that evaluates the influence on urologist treatment recommendations for patients at risk of metastasis using a genomic-based prediction model (DecipherTM). Methods: A prospective, pre-post design was used to assess urologist treatment recommendations following RP in both the adjuvant (without any evidence of PSA rise) and salvage (BCR) settings. Urologists were presented de-identified pathology reports and genomic classifier (GC) test results for 24 patients from a previously conducted GC validation study in high-risk post-RP men. Participants were fellowship trained, high-volume urologic oncologists (n=21) from 18 US institutions. Treatment recommendations for secondary therapy were made based solely on clinical information (pre-GC) and then with genomic biomarker information (post-GC). This study was approved by an independent IRB. Results: Treatment recommendations changed from pre-GC to post-GC in 43% of adjuvant, and in 53% of salvage setting case evaluations. In the adjuvant setting, urologists changed their treatment recommendations from treatment (i.e. radiation and/or hormones) to close observation post-GC in 27% of cases. For cases with low GC risk (<3% risk of metastasis), observation was recommended for 79% of the case evaluations post-GC. Consistent trends were observed in the salvage setting. Conclusion: These results indicate that urologists across a range of practice settings are likely to change treatment decisions when presented with genomic biomarker information following RP. Implementation of genomic risk stratification into routine clinical practice may better direct treatment decision-making post-RP.Oncotarget 04/2013; 4(4). · 6.63 Impact Factor
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ABSTRACT: Abstract Introduction: The 12-gene colon cancer Recurrence Score assay is a clinically validated predictor of recurrence risk in stage II colon cancer patients. We performed a survey characterizing the assay's impact on treatment recommendations for these patients. Methods: U.S. medical oncologists (N=346) who ordered the assay for ≥3 stage II colon cancer patients were asked to complete a web-based survey regarding their most recent such patient. Physicians surveyed represented users of the assay within the first two years of commercial availability which may include 'early adopters.' Results: Most of 116 eligible physicians were in community practice (86%), with median 14.5 years' experience (range, 2-40). Mean patient age was 61 years (range, 32-85); 81% had T3 disease and 38% had comorbidities. Of 76 patients tested for mismatch-repair/microsatellite-instability (MMR/MSI), 13 (17%) were MMR-deficient/MSI-high; 46 (61%) MMR-proficient/MSI-low; and 17 (22%) unknown. Most patients (84%) had ≥12 nodes examined. Median Recurrence Score result was 20 (range, 1-77). Before assay, treatment recommendations were specified for 92 (79%) patients, with no recommendation for 24 (21%). Of the 92 with pre-assay recommendations, chemotherapy was planned for 52 (57%) and observation for 40 (43%); the assay changed recommendations for 27 (29%). Treatment intensity decreased for 18 (67%) and increased for 9 (33%) patients; it was more likely to decrease for lower Recurrence Score values and increase for higher values (P<0.001). Conclusion: For stage II colon cancer patients receiving Recurrence Score testing, 29% of treatment recommendations were changed. Use of the assay may lead to reductions in treatment intensity. Study limitations include retrospective design, data gathering during the first 2 years of assay availability only, and potential non-representativeness of respondents.Current Medical Research and Opinion 10/2013; 30(2). DOI:10.1185/03007995.2013.855183 · 2.37 Impact Factor