Validation of the Prognostic Performance of Breast Cancer Index in Hormone Receptor-Positive Postmenopausal Breast Cancer Patients in the TEAM Trial.

PURPOSE: Patients with early-stage hormone receptor-positive (HR+) breast cancer face a prolonged risk of recurrence even after adjuvant endocrine therapy. The Breast Cancer Index (BCI) is significantly prognostic for overall (0-10 years) and late (5-10 years) distant recurrence (DR) risk in N0 and N1 patients. Here, BCI prognostic performance was evaluated in HR+ postmenopausal women from the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial. EXPERIMENTAL DESIGN: 3,544 patients were included in the analysis (N = 1,519 N0, N = 2,025 N+). BCI risk groups were calculated using pre-specified cutoff points. Kaplan-Meier analyses and log-rank tests were used to assess the prognostic significance of BCI risk groups based on DR. Hazard ratios (HR) and confidence intervals (CI) were calculated using Cox models with and without clinical covariates. RESULTS: For overall 10-year DR, BCI was significantly prognostic in Ni0 (N = 1,196) and N1 (N = 1,234) patients who did not receive prior chemotherapy (P < 0.001). In patients who were DR-free for 5 years, 10-year late DR rates for low- and high-risk groups were 5.4% and 9.3% (N0 cohort, N = 1,285) and 4.8% and 12.2% (N1 cohort, N = 1,625) with multivariate HRs of 2.25 (95% CI, 1.30-3.88; P = 0.004) and 2.67 (95% CI, 1.53-4.63; P < 0.001), respectively. Late DR performance was substantially improved using previously optimized cutoff points, identifying BCI low-risk groups with even lower 10-year late DR rates of 3.8% and 2.7% in N0 and N1 patients, respectively. CONCLUSIONS: The TEAM trial represents the largest prognostic validation study for BCI to date and provides a more representative assessment of late DR risk to guide individualized treatment decision-making for HR+ patients with early-stage breast cancer.

Correlative studies of the Breast Cancer Index (HOXB13/IL17BR) and ER, PR, AR, AR/ER ratio and Ki67 for prediction of extended endocrine therapy benefit: a Trans-aTTom study.

BACKGROUND: Multiple clinical trials demonstrate consistent but modest benefit of adjuvant extended endocrine therapy (EET) in HR + breast cancer patients. Predictive biomarkers to identify patients that benefit from EET are critical to balance modest reductions in risk against potential side effects of EET. This study compares the performance of the Breast Cancer Index, BCI (HOXB13/IL17BR, H/I), with expression of estrogen (ER), progesterone (PR), and androgen receptors (AR), and Ki67, for prediction of EET benefit. METHODS: Node-positive (N+) patients from the Trans-aTTom study with available tissue specimen and BCI results (N = 789) were included. Expression of ER, PR, AR, and Ki67 was assessed by quantitative immunohistochemistry. BCI (H/I) gene expression analysis was conducted by quantitative RT-PCR. Statistical significance of the treatment by biomarker interaction was evaluated by likelihood ratio tests based on multivariate Cox proportional models, adjusting for age, tumor size, grade, and HER2 status. Pearson's correlation coefficients were calculated to evaluate correlations between BCI (H/I) versus ER, PR, AR, Ki67 and AR/ER ratio. RESULTS: EET benefit, measured by the difference in risk of recurrence between patients treated with tamoxifen for 10 versus 5 years, is significantly associated with increasing values of BCI (H/I) (interaction P = 0.01). In contrast, expression of ER (P = 0.83), PR (P = 0.66), AR (P = 0.78), Ki67 (P = 0.87) and AR/ER ratio (P = 0.84) exhibited no significant relationship with EET benefit. BCI (H/I) showed a very weak negative correlation with ER (r = - 0.18), PR (r = - 0.25), and AR (r = - 0.14) expression, but no correlation with either Ki67 (r = 0.04) or AR/ER ratio (r = 0.02). CONCLUSION: These findings are consistent with the growing body of evidence that BCI (H/I) is significantly predictive of response to EET and outcome. Results from this direct comparison demonstrate that expression of ER, PR, AR, Ki67 or AR/ER ratio are not predictive of benefit from EET. BCI (H/I) is the only clinically validated biomarker that predicts EET benefit.

Breast Cancer Index Is a Predictive Biomarker of Treatment Benefit and Outcome from Extended Tamoxifen Therapy: Final Analysis of the Trans-aTTom Study.

PURPOSE: The Breast Cancer Index (BCI) HOXB13/IL17BR (H/I) ratio predicts benefit from extended endocrine therapy in hormone receptor-positive (HR+) early-stage breast cancer. Here, we report the final analysis of the Trans-aTTom study examining BCI (H/I)'s predictive performance. EXPERIMENTAL DESIGN: BCI results were available for 2,445 aTTom trial patients. The primary endpoint of recurrence-free interval (RFI) and secondary endpoints of disease-free interval (DFI) and disease-free survival (DFS) were examined using Cox proportional hazards regression and log-rank test. RESULTS: Final analysis of the overall study population (N = 2,445) did not show a significant improvement in RFI with extended tamoxifen [HR, 0.90; 95% confidence interval (CI), 0.69-1.16; P = 0.401]. Both the overall study population and N0 group were underpowered due to the low event rate in the N0 group. In a pre-planned analysis of the N+ subset (N = 789), BCI (H/I)-High patients derived significant benefit from extended tamoxifen (9.7% absolute benefit: HR, 0.33; 95% CI, 0.14-0.75; P = 0.016), whereas BCI (H/I)-Low patients did not (-1.2% absolute benefit; HR, 1.11; 95% CI, 0.76-1.64; P = 0.581). A significant treatment-to-biomarker interaction was demonstrated on the basis of RFI, DFI, and DFS (P = 0.037, 0.040, and 0.025, respectively). BCI (H/I)-High patients remained predictive of benefit from extended tamoxifen in the N+/HER2- subgroup (9.4% absolute benefit: HR, 0.35; 95% CI, 0.15-0.81; P = 0.047). A three-way interaction evaluating BCI (H/I), treatment, and HER2 status was not statistically significant (P = 0.849). CONCLUSIONS: Novel findings demonstrate that BCI (H/I) significantly predicts benefit from extended tamoxifen in HR+ N+ patients with HER2- disease. Moreover, BCI (H/I) demonstrates significant treatment to biomarker interaction across survival outcomes.

Analysis of the effect of various decalcification agents on the quantity and quality of nucleic acid (DNA and RNA) recovered from bone biopsies.

Molecular studies are part of standard care for cancer patients. Bone, a common and sometimes sole site of metastasis, requires decalcification for morphological examination. Many commonly used decalcification agents contain strong acids that degrade nucleic acids. The paradigm shift in oncology, with biomarker targeted therapy and gene expression profiling analysis, requires sufficient nucleic acid recovery from bone biopsy specimens. We systematically studied the effects of a spectrum of decalcification agents on the quantity and quality of RNA and DNA recovered from bone biopsies. Multiple bone biopsies of similar size and cellularity were fixed in 10% neutral-buffered formalin, randomized to various decalcification agents for 2 hours then processed, and embedded. Tissue lysates were obtained from unstained sections and nucleic acid isolated. DNA and RNA were quantified. Assessment of DNA and RNA integrity was accomplished by comparison of the average cycle threshold by polymerase chain reaction of selected housekeeping genes for each agent. Results were then analyzed by 2-sample t test. There was a significant decrease in both DNA and RNA yield and integrity with strong acids (hydrochloric, nitric) vs 14% EDTA and formic acid. DNA yield was (mean nanograms) 6.15 vs 68.68 (P<.001) and RNA was (mean nanograms) 121.53 vs 288.89 (P=.003), respectively. DNA integrity (mean cycle threshold) was 35.79 vs 30.16 (P<.001), and RNA was 33.03 vs 26.5 (P<.001), respectively. Decalcification of bone biopsies with EDTA or formic acid agents was associated with a significant improvement in recovered nucleic acid quantity and quality.

Development and validation of a 32-gene prognostic index for prostate cancer progression.

The accurate determination of the risk of cancer recurrence is an important unmet need in the management of prostate cancer. Patients and physicians must weigh the benefits of currently available therapies against the potential morbidity of these treatments. Herein we describe the development of a gene expression-based continuous risk index and a validation of this test in an independent, blinded cohort of post-radical prostatectomy (RP) patients. A gene expression signature, prognostic for prostate-specific antigen (PSA) recurrence, was identified through a bioinformatic analysis of the expression of 1,536 genes in malignant prostate tissue from a training cohort of consecutive patients treated with RP. The assay was transferred to a real-time RT-PCR platform, and a continuous risk index model was constructed based on the expression of 32 genes. This 32-gene risk index model was validated in an independent, blinded cohort of 270 RP patients. In multivariate analyses, the risk index was prognostic for risk of PSA recurrence and had added value over standard prognostic markers such as Gleason score, pathologic tumor stage, surgical margin status, and presurgery PSA (hazard ratio, 4.05; 95% confidence interval, 1.50-10.94; P = 0.0057). Furthermore, RP patients could be stratified based on the risk of PSA recurrence and the development of metastatic disease. The 32-gene signature identified here is a robust prognostic marker for disease recurrence. This assay may aid in postoperative treatment selection and has the potential to impact decision making at the biopsy stage.

Performance and clinical evaluation of the 92-gene real-time PCR assay for tumor classification.

Accurate determination of cancer origin is necessary to guide optimal treatment but remains a diagnostic challenge. Gene expression profiling technologies have aided the classification of tumors and, therefore, could be applied in conjunction with clinicopathologic correlates to improve accuracy. We report an expanded version of the previously described 92-gene assay to classify 30 main tumor types and 54 histological subtypes, with coverage of ≥95% of all solid tumors based on incidence. Increased tissue coverage was achieved through expansion of a reference tumor database containing 2,206 specimens, with a median of 62 samples per main tumor type. The 92-gene classification algorithm demonstrated sensitivities of 87% and 85% for 30 main types and 54 histological subtypes, respectively, in leave-one-out cross validation, and 83% in a test set of 187 tumors representing 28 of the 30 main cancer types. These findings provide further support that broad and diverse tumor classification can be performed using a relatively compact gene set. An additional 300 consecutive cases submitted for clinical testing were profiled to characterize clinical utility in a real-world setting: the 92-gene assay confirmed 78% of samples having a single suspected primary tumor and provided a single molecular prediction in 74% of cases with two or more differential diagnoses. Further development of the 92-gene RT-PCR assay has resulted in a significant expansion in reportable tumor types and histological features with strong performance characteristics and supports the use of molecular classification as an objective standardized adjunct to current methods.

A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer.

PURPOSE: Histologic tumor grade is a well-established prognostic factor for breast cancer, and tumor grade-associated genes are the common denominator of many prognostic gene signatures. The objectives of this study are as follows: (a) to develop a simple gene expression index for tumor grade (molecular grade index or MGI), and (b) to determine whether MGI and our previously described HOXB13:IL17BR index together provide improved prognostic information. EXPERIMENTAL DESIGN: From our previously published list of genes whose expression correlates with both tumor grade and tumor stage progression, we selected five cell cycle-related genes to build MGI and evaluated MGI in two publicly available microarray data sets totaling 410 patients. Using two additional cohorts (n = 323), we developed a real-time reverse transcription PCR assay for MGI, validated its prognostic utility, and examined its interaction with HOXB13:IL17BR. RESULTS: MGI performed consistently as a strong prognostic factor and was comparable with a more complex 97-gene genomic grade index in multiple data sets. In patients treated with endocrine therapy, MGI and HOXB13:IL17BR modified each other's prognostic performance. High MGI was associated with significantly worse outcome only in combination with high HOXB13:IL17BR, and likewise, high HOXB13:IL17BR was significantly associated with poor outcome only in combination with high MGI. CONCLUSIONS: We developed and validated a five-gene reverse transcription PCR assay for MGI suitable for analyzing routine formalin-fixed paraffin-embedded clinical samples. The combination of MGI and HOXB13:IL17BR outperforms either alone and identifies a subgroup ( approximately 30%) of early stage estrogen receptor-positive breast cancer patients with very poor outcome despite endocrine therapy.

Molecular classification of human cancers using a 92-gene real-time quantitative polymerase chain reaction assay.

CONTEXT: Correct diagnosis of the tissue origin of a metastatic cancer is the first step in disease management, but it is frequently difficult using standard pathologic methods. Microarray-based gene expression profiling has shown great promise as a new tool to address this challenge. OBJECTIVE: Adoption of microarray technologies in the clinic remains limited. We aimed to bridge this technological gap by developing a real-time quantitative polymerase chain reaction (RT-PCR) assay. DESIGN: We constructed a microarray database of 466 frozen and 112 formalin-fixed, paraffin-embedded (FFPE) samples of both primary and metastatic tumors, measuring expression of 22,000 genes. From the microarray database, we used a genetic algorithm to search for gene combinations optimal for multitumor classification. A 92-gene RT-PCR assay was then designed and used to generate a database for 481 frozen and 119 FFPE tumor samples. RESULTS: The microarray-based K-nearest neighbor classifier demonstrated 84% accuracy in classifying 39 tumor types via cross-validation and 82% accuracy in predicting 112 independent FFPE samples. We successfully translated the microarray database to the RT-PCR platform, which allowed an overall success rate of 87% in classifying 32 different tumor classes in the validation set of 119 FFPE tumor samples. CONCLUSIONS: The RT-PCR-based expression assay involving 92 genes represents a powerful tool for accurately and objectively identifying the site of origin for metastatic tumors, especially in the cases of cancer of unknown primary. The assay uses RT-PCR and routine FFPE samples, making it suitable for rapid clinical adoption.

A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen.

Tamoxifen significantly reduces tumor recurrence in certain patients with early-stage estrogen receptor-positive breast cancer, but markers predictive of treatment failure have not been identified. Here, we generated gene expression profiles of hormone receptor-positive primary breast cancers in a set of 60 patients treated with adjuvant tamoxifen monotherapy. An expression signature predictive of disease-free survival was reduced to a two-gene ratio, HOXB13 versus IL17BR, which outperformed existing biomarkers. Ectopic expression of HOXB13 in MCF10A breast epithelial cells enhances motility and invasion in vitro, and its expression is increased in both preinvasive and invasive primary breast cancer. The HOXB13:IL17BR expression ratio may be useful for identifying patients appropriate for alternative therapeutic regimens in early-stage breast cancer.

Gene expression profiles of human breast cancer progression.

Although distinct pathological stages of breast cancer have been described, the molecular differences among these stages are largely unknown. Here, through the combined use of laser capture microdissection and DNA microarrays, we have generated in situ gene expression profiles of the premalignant, preinvasive, and invasive stages of human breast cancer. Our data reveal extensive similarities at the transcriptome level among the distinct stages of progression and suggest that gene expression alterations conferring the potential for invasive growth are already present in the preinvasive stages. In contrast to tumor stage, different tumor grades are associated with distinct gene expression signatures. Furthermore, a subset of genes associated with high tumor grade is quantitatively correlated with the transition from preinvasive to invasive growth.