Discrepancy in risk assessment of hormone receptor positive early-stage breast cancer patients using breast cancer index and recurrence score.

PURPOSE: A recent comparison of the prognostic accuracy of Breast Cancer Index (BCI) and the Recurrence Score (RS) showed that BCI was more precise than RS. BCI identified a subset of RS low and intermediate risk patients with clinically relevant elevated rates of distant recurrences (DR). The current study analyzed the correlation of BCI and RS risk classification to clinical and pathological parameters and further examined the re-categorization between the two risk group indices in a multi-institutional cohort of hormone receptor positive (HR+) breast cancer patients. METHODS: 560 women with HR+, lymph node-negative breast cancer who underwent testing with RS as part of their routine clinical care were included in the final analysis. Individual risk was assessed using predefined categories of RS and BCI (Low, Intermediate and High, respectively). Correlations between BCI, RS, and standard clinical-pathological prognostic factors were examined, and re-categorization of risk groups between BCI and RS was analyzed. RESULTS: An overall significant association between histological tumor grade and RS or BCI was observed with high-grade tumors more prevalent among RS and BCI high-risk patients. The invasive ductal carcinoma histologic subtype was associated with 98% and 93% of high-risk RS and BCI cases, respectively. The invasive lobular subtype accounted for 0% and 6% of high-risk RS and BCI cases, respectively. A poor agreement between the two biomarker risk group indices was demonstrated with more than 51% of the total cohort stratified differently between BCI and RS. As compared with RS, BCI stratified fewer patients into the intermediate-risk group (29% vs. 39%, BCI and RS, respectively) and more patients into the high-risk group (19% vs. 7%, BCI and RS, respectively). Subsets of both RS low- and intermediate-risk patients were identified by BCI as high risk. CONCLUSIONS: In this clinical series, BCI and RS risk groups demonstrated a significant association with histological tumor grade. BCI showed a modest correlation with tumor size and no correlation with age, while RS showed no correlation with tumor size or age. Compared with RS, BCI classifies fewer intermediate risk patients, identifies subsets of low and intermediate RS risk patients as high-risk, and provides distinct individualized risk assessment for patients with early-stage breast cancer.

Ultrasensitive plasma ctDNA KRAS assay for detection, prognosis, and assessment of therapeutic response in patients with unresectable pancreatic ductal adenocarcinoma.

Precision oncology requires sensitive and specific clinical biomarkers. Carbohydrate Antigen 19-9 (CA19-9) is widely used in pancreatic ductal adenocarcinoma (PDA) but lacks sensitivity and specificity. Nearly all PDAs harbor somatic KRAS mutations, nominating circulating tumor DNA (ctDNA) KRAS as an alternative disease biomarker, however, variable clinical performance has limited its clinical utility. We applied an ultrasensitive, PCR mutation enrichment, next generation sequencing ctDNA KRAS assay in a large cohort of patients with unresectable PDA (N = 189) recruited to the BIOPAC study between 2008-2015. Baseline and longitudinal serum CA19-9 and plasma ctDNA KRAS were correlated with time to progression (TTP) and overall survival (OS). Baseline ctDNA KRAS detection rate was 93.7% (86.4% in patients with non-elevated CA19-9). ctDNA KRAS and CA19-9 were positively correlated yet independently associated with TTP and OS (ctDNA KRAS p = 0.0018 and 0.0014; CA19-9 p = 0.0294 and 0.0007, respectively). A generated model quantitating longitudinal ctDNA KRAS correctly assessed greater than 80% of patient responses. Quantitative detection of KRAS ctDNA is an informative prognostic biomarker, complementary to CA19-9 in patients with unresectable PDA. Longitudinal ctDNA KRAS may inform therapeutic decision making and provides a kinetically dynamic and quantitative metric of patient response.

Assessment of the prognostic and predictive utility of the Breast Cancer Index (BCI): an NCIC CTG MA.14 study.

BACKGROUND: Biomarkers that can be used to accurately assess the residual risk of disease recurrence in women with hormone receptor-positive breast cancer are clinically valuable. We evaluated the prognostic value of the Breast Cancer Index (BCI), a continuous risk index based on a combination of HOXB13:IL17BR and molecular grade index, in women with early breast cancer treated with either tamoxifen alone or tamoxifen plus octreotide in the NCIC MA.14 phase III clinical trial (ClinicalTrials.gov Identifier NCT00002864; registered 1 November 1999). METHODS: Gene expression analysis of BCI by real-time polymerase chain reaction was performed blinded to outcome on RNA extracted from archived formalin-fixed, paraffin-embedded tumor samples of 299 patients with both lymph node-negative (LN-) and lymph node-positive (LN+) disease enrolled in the MA.14 trial. Our primary objective was to determine the prognostic performance of BCI based on relapse-free survival (RFS). MA.14 patients experienced similar RFS on both treatment arms. Association of gene expression data with RFS was evaluated in univariate analysis with a stratified log-rank test statistic, depicted with a Kaplan-Meier plot and an adjusted Cox survivor plot. In the multivariate assessment, we used stratified Cox regression. The prognostic performance of an emerging, optimized linear BCI model was also assessed in a post hoc analysis. RESULTS: Of 299 samples, 292 were assessed successfully for BCI for 146 patients accrued in each MA.14 treatment arm. BCI risk groups had a significant univariate association with RFS (stratified log-rank p = 0.005, unstratified log-rank p = 0.007). Adjusted 10-year RFS in BCI low-, intermediate-, and high-risk groups was 87.5 %, 83.9 %, and 74.7 %, respectively. BCI had a significant prognostic effect [hazard ratio (HR) 2.34, 95 % confidence interval (CI) 1.33-4.11; p = 0.004], although not a predictive effect, on RFS in stratified multivariate analysis, adjusted for pathological tumor stage (HR 2.22, 95 % CI 1.22-4.07; p = 0.01). In the post hoc multivariate analysis, higher linear BCI was associated with shorter RFS (p = 0.002). CONCLUSIONS: BCI had a strong prognostic effect on RFS in patients with early-stage breast cancer treated with tamoxifen alone or with tamoxifen and octreotide. BCI was prognostic in both LN- and LN+ patients. This retrospective study is an independent validation of the prognostic performance of BCI in a prospective trial.

Health economic analysis of Breast Cancer Index in patients with ER+, LN- breast cancer.

OBJECTIVES: Breast Cancer Index (BCI) is a novel gene expression-based test for patients with estrogen receptor positive (ER+), lymph node negative (LN-) breast cancer that predicts risk of recurrence over 10 years, and also specifically predicts risk of late (≥5 y) recurrences and likelihood of benefit from extended (≥5 y) endocrine therapy. The objective of this study was to evaluate cost utility of BCI from a US third-party payer perspective. STUDY DESIGN: Two fact-based economic models were developed to project the cost and effectiveness of BCI in a hypothetical population of patients with ER+, LN- breast cancer compared with standard clinicopathologic diagnostic modalities. METHODS: Costs associated with adjuvant chemotherapy, toxicity, followup, endocrine therapy, and recurrence were modeled over 10 years. The models examined cost utility compared with standard practice when used at diagnosis and in patients disease-free at 5 years post diagnosis. RESULTS: Use of BCI was projected to be cost saving in both models. In the newly diagnosed population, net cost savings were $3803 per patient tested. In the 5 years post diagnosis population, BCI was projected to yield a net cost savings of $1803 per patient tested. Sensitivity analyses demonstrated that BCI was cost saving across a wide range of clinically relevant input assumptions. CONCLUSIONS: BCI was projected to be cost saving when used either at diagnosis or at 5 years post diagnosis. Cost savings are achieved through projected impact on adjuvant chemotherapy use, extended endocrine therapy use, and endocrine therapy compliance. These findings require validation in additional cohorts, including studies of real-world clinical practice.

Cost effectiveness of a 92-gene assay for the diagnosis of metastatic cancer.

OBJECTIVES: To estimate the clinical and economic trade-offs involved in using a molecular assay (92-gene assay, CancerTYPE ID) to aid in identifying the primary site of difficult-to-diagnose metastatic cancers and to explore whether the 92-gene assay can be used to standardize the diagnostic process and costs for clinicians, patients, and payers. METHODS: Four decision-analytic models were developed to project the lifetime clinical and economic impact of incorporating the 92-gene assay compared with standard care alone. For each model, total and incremental costs, life-years, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios (ICERs), and the proportion of patients treated correctly versus incorrectly were projected from the payer perspective. Model inputs were based on published literature, analyses of SEER (Surveillance Epidemiology and End RESULTS) data, publicly available data, and interviews with clinical experts. RESULTS: In all four models, the 92-gene assay increased the proportion of patients treated correctly, decreased the proportion of patients treated with empiric therapy, and increased quality-adjusted survival. In the primary model, the ICER was $50,273/QALY; thus, the 92-gene assay is therefore cost effective when considering a societal willingness-to-pay threshold of $100,000/QALY. These findings were robust across sensitivity analyses. CONCLUSIONS: Use of the 92-gene assay for diagnosing metastatic tumors of uncertain origin is associated with reduced misdiagnoses, increased survival, and improved quality of life. Incorporating the assay into current practice is a cost-effective approach to standardizing diagnostic methods while improving patient care. Limitations of this analysis are the lack of data availability and resulting modeling simplifications, although sensitivity analyses showed these to not be key drivers of results.