Amy S. Clark

Characteristics Associated With Differences in Survival Among Black and White Women With Breast Cancer

IMPORTANCE Difference in breast cancer survival by race is a recognized problem among Medicare beneficiaries. OBJECTIVE To determine if racial disparity in breast cancer survival is primarily attributable to differences in presentation characteristics at diagnosis or subsequent treatment. DESIGN, SETTING, AND PATIENTS Comparison of 7375 black women 65 years and older diagnosed between 1991 to 2005 and 3 sets of 7375 matched white control patients selected from 99,898 white potential controls, using data for 16 US Surveillance, Epidemiology and End Results (SEER) sites in the SEER-Medicare database. All patients received follow-up through December 31, 2009, and the black case patients were matched to 3 white control populations on demographics (age, year of diagnosis, and SEER site), presentation (demographics variables plus patient comorbid conditions and tumor characteristics such as stage, size, grade, and estrogen receptor status), and treatment (presentation variables plus details of surgery, radiation therapy, and chemotherapy). MAIN OUTCOMES AND MEASURES 5-Year survival. RESULTS The absolute difference in 5-year survival (blacks, 55.9%; whites, 68.8%) was 12.9% (95% CI, 11.5%-14.5%; P < .001) in the demographics match. This difference remained unchanged between 1991 and 2005. After matching on presentation characteristics, the absolute difference in 5-year survival was 4.4% (95% CI, 2.8%-5.8%; P < .001) and was 3.6% (95% CI, 2.3%-4.9%; P < .001) lower for blacks than for whites matched also on treatment. In the presentation match, fewer blacks received treatment (87.4% vs 91.8%; P < .001), time from diagnosis to treatment was longer (29.2 vs 22.8 days; P < .001), use of anthracyclines and taxols was lower (3.7% vs 5.0%; P < .001), and breast-conserving surgery without other treatment was more frequent (8.2% vs 7.3%; P = .04). Nevertheless, differences in survival associated with treatment differences accounted for only 0.81% of the 12.9% survival difference. CONCLUSIONS AND RELEVANCE In the SEER-Medicare database, differences in breast cancer survival between black and white women did not substantially change among women diagnosed between 1991 and 2005. These differences in survival appear primarily related to presentation characteristics at diagnosis rather than treatment differences.

CDK 4/6 Inhibitor Palbociclib (PD0332991) in Rb+ Advanced Breast Cancer: Phase II Activity, Safety, and Predictive Biomarker Assessment

Purpose: The G1–S checkpoint of the cell cycle is frequently dysregulated in breast cancer. Palbociclib (PD0332991) is an oral inhibitor of CDK4/6. Based upon preclinical/phase I activity, we performed a phase II, single-arm trial of palbociclib in advanced breast cancer. Experimental Design: Eligible patients had histologically confirmed, metastatic breast cancer positive for retinoblastoma (Rb) protein and measureable disease. Palbociclib was given at 125 mg orally on days 1 to 21 of a 28-day cycle. Primary objectives were tumor response and tolerability. Secondary objectives included progression-free survival (PFS) and assessment of Rb expression/localization, KI-67, p16 loss, and CCND1 amplification. Results: Thirty-seven patients were enrolled; 84% hormone-receptor (HR)+/Her2−, 5% HR+/Her2+, and 11% HR−/Her2−, with a median of 2 prior cytotoxic regimens. Two patients had partial response (PR) and 5 had stable disease ≥ 6 months for a clinical benefit rate (CBR = PR + 6moSD) of 19% overall, 21% in HR+, and 29% in HR+/Her2− who had progressed through ≥2 prior lines of hormonal therapy. Median PFS overall was 3.7 months [95% confidence interval (CI), 1.9–5.1], but significantly longer for those with HR+ versus HR− disease (P = 0.03) and those who had previously progressed through endocrine therapy for advanced disease (P = 0.02). Grade 3/4 toxicities included neutropenia (51%), anemia (5%), and thrombocytopenia (22%). Twenty-four percent had treatment interruption and 51% had dose reduction, all for cytopenias. No biomarker identified a sensitive tumor population. Conclusions: Single-agent palbociclib is well tolerated and active in patients with endocrine-resistant, HR+, Rb-positive breast cancer. Cytopenias were uncomplicated and easily managed with dose reduction. Clin Cancer Res; 21(5); 995–1001. ©2014 AACR.

The Neoadjuvant Model is Still the Future for Drug Development in Breast Cancer

The many improvements in breast cancer therapy in recent years have so lowered rates of recurrence that it is now difficult or impossible to conduct adequately powered adjuvant clinical trials. Given the many new drugs and potential synergistic combinations, the neoadjuvant approach has been used to test benefit of drug combinations in clinical trials of primary breast cancer. A recent FDA-led meta-analysis showed that pathologic complete response (pCR) predicts disease-free survival (DFS) within patients who have specific breast cancer subtypes. This meta-analysis motivated the FDAs draft guidance for using pCR as a surrogate endpoint in accelerated drug approval. Using pCR as a registration endpoint was challenged at ASCO 2014 Annual Meeting with the presentation of ALTTO, an adjuvant trial in HER2-positive breast cancer that showed a nonsignificant reduction in DFS hazard rate for adding lapatinib, a HER-family tyrosine kinase inhibitor, to trastuzumab and chemotherapy. This conclusion seemed to be inconsistent with the results of NeoALTTO, a neoadjuvant trial that found a statistical improvement in pCR rate for the identical lapatinib-containing regimen. We address differences in the two trials that may account for discordant conclusions. However, we use the FDA meta-analysis to show that there is no discordance at all between the observed pCR difference in NeoALTTO and the observed HR in ALTTO. This underscores the importance of appropriately modeling the two endpoints when designing clinical trials. The I-SPY 2/3 neoadjuvant trials exemplify this approach. Clin Cancer Res; 21(13); 2911–5. ©2015 AACR.

Molecular Imaging Biomarkers for Oncology Clinical Trials

Biomarkers can be used to characterize disease status or predict disease behavior. Cancer biomarkers have typically relied on assays of blood or tissue; however, molecular imaging has a promising and complementary role as a cancer biomarker. This “Focus on Molecular Imaging” article reviews the current role of biomarkers to direct cancer clinical trials and clinical practice, along with current and future cancer biomarker applications of molecular imaging.

Racial disparities in colon cancer survival: a matched cohort study.

Context Black patients have decreased colon cancer survival compared with white patients. Contribution In a model that sequentially matched patients with colon cancer by demographic characteristics, then presentation, and then treatment, little of the racial difference in colon cancer survival was found to be due to differences in treatment. Caution Only patients covered by Medicare were studied. Implication Efforts to decrease racial disparities in colon cancer survival may be best focused on prevention and early detection of disease. The Editors With nearly 100000 new cases each year, colon cancer is the fourth-most common cancer in the United States and is also responsible for the second-highest number of deaths with approximately 50000 per year (1). The incidence of colon cancer is highest among black persons (2), and racial disparities in survival among patients with colon cancer have long existed (36). Numerous reports have not only identified and documented worse outcomes in black patients with colon cancer but have suggested potential reasons for the disparity based on differences in screening (7, 8), comorbid conditions on presentation (9), stage (1012), treatment (1315), and socioeconomic status (16). In the Medicare population as a whole, life tables indicate a disparity between black and white patients in 5-year survival at age 65 years of 3.6% (17), but this widens substantially when a patient develops a serious illness, such as colon cancer (6). Although we examined the extent of the racial disparity in colon cancer survival in the Medicare population, the main purpose is to understand the nature of the disparity. We asked whether white patients who present like black patients are treated as black patients are treated, and if not, to what extent a disparity in treatment explains the disparity in survival. We assessed the magnitude of the disparity; examined whether the disparity has changed from 1998 and before (1991 to 1998) to 1999 and after (1999 to 2005), determined the relative contributions of presentation at diagnosis (and treatment after presentation) to differences in survival experienced by these groups, and explored how socioeconomic variables relate to the overall disparity. Our goal was to assist in determining which paths should be pursued to eliminate the persistent racial disparity in colon cancer survival. Methods Patient Population This research protocol was approved by the Institutional Review Board of The Childrens Hospital of Philadelphia (Philadelphia, Pennsylvania). We obtained the Survey, Epidemiology, and End Results (SEER)Medicare database for the years 1991 to 2005 for 16 SEER sites throughout the United States, including all sites except the Alaska Native Tumor Registry. There were 88858 patients aged 65 years or older with newly diagnosed invasive colon cancer. For each patient, the SEER Patient Entitlement and Diagnosis Summary File (18, 19) was merged with Medicare Parts A and B, outpatient claims, and the beneficiary summary file (which was updated to 31 December 2009 for this data set), providing a minimum of 4 years of follow-up for all patients. For all analyses of trends over time, we examined the 12 SEER sites that were collecting data during the entire study. For analyses that did not consider trends over time, we used all 16 sites. Defining Patient Characteristics We defined race by using the SEER algorithm (20) and compared black with white non-Hispanic and white Hispanic patients for the primary analysis. Patient comorbid conditions, such as congestive heart failure, diabetes, past acute myocardial infarction, stroke, hypertension, and 21 other conditions noted in the Supplement, were defined with International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes (2124) and collected from Medicare claims (inpatient, outpatient, and physician bills) during a 3-month period before diagnosis. Supplement. Racial Disparities in Colon Cancer Survival: Extended Analyses Tumor Biology Characteristics of the patients tumor, including stage, grade, number of nodes dissected, and number of positive nodes, were obtained from the SEER Patient Entitlement and Diagnosis Summary File. For patients with stage II colon cancer, we used SEER and Medicare data to define 2 strata (high and low) of risk for recurrence (2527), on the basis of the presence of 1 of the following prognostic indicators: T4 tumor status, perforation, and fewer than 10 nodes removed (Supplement). Treatment Variables We defined treatment on the basis of information from both SEER and Medicare data. Surgery was defined by billing codes in the Medicare files. Evidence of chemotherapy was also determined by Medicare billing codes. Radiation therapy was determined by Medicare billing codes and information from the SEER Patient Entitlement and Diagnosis Summary File (Supplement). Statistical Analysis Similar to our previously published work (28), this analysis used tapered multivariate matching (2830) to compare the entire population of black patients in SEER-Medicare with 3 white populations individually paired to the black population to answer various questions about the origins of the racial disparity. We used all black patients for each match, so the black population was unchanged and fully representative of black patients in the SEER population. The white population changed according to the variables used in the match. We created 3 overlapping (30) matched analyses: a demographic characteristics match, which matched white to black patients by SEER site, age, sex, and year of diagnosis; a presentation match, which matched black and white patients by demographic characteristics as well as presentation characteristics, comorbid conditions, and tumor biology (stage, including high- and low-risk stage II, grade, and nodes); and a treatment match, which included matching variables from demographic characteristics and presentation as well as relevant treatment variables, including surgery, chemotherapy, radiation therapy, and individual types of chemotherapy. The hazards of adjustments made by models rather than matching are discussed by Rubin (31), Hansen (32), Stuart (33), and Lu and colleagues (34). As suggested by Rubin and Rosenbaum (3537), matching was performed first without viewing outcomes. The PROC ASSIGN (38) function in SAS, version 9.2 (SAS Institute), was used for all matching, providing optimal matches that minimized the total distance within matched pairs (37). We used near-fine balance for the SEER site in the presentation and treatment matches (28, 39, 40). This meant that each site contributed nearly identical numbers of white and black patients to each matched analysis. Matching on patient covariates in the presentation and treatment matches also included a score predicting black race (a propensity score) and a risk score based on a Charlson score (4144). The propensity scores for the presentation and treatment matches came from a logistic regression of white-versus-black race using all of the variables to be controlled in the specific match. Matching on the propensity score tends to balance variables making up the propensity score (37, 4547). For each matching variable, we checked similarities between black and white patients using the standardized difference in means before and after matching, which is the mean difference between groups in units of the before matching SDs (22, 48, 49). A conventional rule of thumb aims for mean standardized differences below 0.2 of an SD (22, 48, 49), although we aimed for standardized differences below 0.1. We also assessed how closely we achieved balance using 2-sample randomization tests, specifically the Wilcoxon rank-sum test for each continuous covariate, the Fisher exact test for each binary covariate, and a single cross-match test for all covariates in a given match (28, 5053). When testing the hypothesis that there were no differences in outcomes between the matched patients, the Wilcoxon sign-rank statistic (54) was calculated for continuous variables and the McNemar statistic (55) was used for binary outcomes. When modeling survival differences over time, we used the paired version of the Cox proportional hazards model (56). When comparing paired survival distributions, we used the PrenticeWilcoxon test (57). We obtained SEs for the paired differences in survival probabilities using the bootstrap method as described by Efron and Tibshirani (58). Differences among white patients were tested using the exterior match that removed overlap in the white control groups (28, 30), again testing for differences in survival using the PrenticeWilcoxon test (57). For all tests of outcomes and matching quality, differences were considered statistically significant if the Pvalue was less than 0.05. For analyses that compared survival in the 2 time periods, we used only the 12 SEER sites that collected data for the full duration of these intervals. Role of the Funding Source The Agency for Healthcare Research and Quality and National Science Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Results Quality of the Matches: Matching Results In Table 1, we report characteristics of the entire black population and 3 white populations matched sequentially to the black population. Table 1. Quality of Matches* The 3 matches sequentially removed aspects of the disparity while leaving other aspects in place so we could develop an understanding of how the disparity occurs. In each match, the variables controlled in that match were closely balanced, with no standardized difference exceeding 0.07 SD. Complete matching tables are provided in the Supplement. In a given match, unmatched variables exhibit differences that reveal aspects of the disparity. For example, among all black patients with colon cancer,

Immunotherapy for Breast Cancer: What Are We Missing?

The recent demonstration of modest single-agent activity of programmed death-ligand 1 (PD-L1) and programmed death receptor-1 (PD-1) antibodies in patients with breast cancer has generated hope that breast cancer can be made amenable to immunotherapy. Depending on the subtype of breast cancer, it is now clear in both primary and metastatic disease that the extent of tumor-infiltrating T cells is not only prognostic for survival but predictive of response to nonimmune, standard therapies. Despite these findings, immune cytolytic activity in spontaneous breast tumors, the burden of nonsynonymous tumor mutations, and the predicted load of neoepitopes—factors linked to response to checkpoint blockade in other malignancies—are all relatively modest in breast cancer compared with melanoma or lung cancer. Thus, in breast cancer, combinations of immune agents with nonredundant mechanisms of action are high-priority strategies. For most breast cancers that exhibit relatively modest T-cell infiltration, major challenges include immune suppression in the tumor microenvironment as well as failed or suboptimal T-cell priming. Agents that trigger de novo T-cell responses may be critical for the successful development of cancer immunotherapy and immune prevention in breast cancer. Success may also require reaching beyond nonsynonymous mutations as the T-cell epitopes to target, especially as numerous unmutated proteins were validated as breast cancer–associated antigens in the pre-checkpoint era. A deeper understanding of the immunobiology of breast cancer will be critical for immunotherapy to become broadly relevant in this disease. Clin Cancer Res; 23(11); 2640–6. ©2017 AACR. See all articles in this CCR Focus section, “Breast Cancer Research: From Base Pairs to Populations.”

How Imaging Biomarkers Can Inform Clinical Trials and Clinical Practice in the Era of Targeted Cancer Therapy.

Individualized therapy for patients with cancer, often termed personalized or precision medicine, has become an increasingly important topic with the recognition that tumors once classified solely by their tissue of origin consist of multiple genetically distinct subgroups. This ability to categorize tumors into small subgroups defined by molecular makeup can have an important impact on treatment approach because some tumors are driven by a molecular abnormality that can be targeted by specific drugs. Targeted therapies are available for tumors that express the estrogen receptor (ER) (tamoxifen, letrozole), HER2 (trastuzumab, pertuzumab), EGFR (gefitinib), the BCR-ABL fusion protein kinase (imatinib), the BRAF V600E protein kinase (vemurafenib), PD-1 ligands (pembrolizumab), and VEGF (bevacizumab), to name a few. However, questions remain for how best to use these therapies. How can patients most likely to benefit from targeted therapy be easily and reliably identified? How should response be measured (especially because many targetedtherapiesarecytostatic)?Historically,thesequestions have been addressed by assaying tissue or serum biomarkers. However, molecular imaging offers several advantages as a cancer biomarker and is complementary to tissue sampling. Because imaging is noninvasive and nondestructive, it is capable of serial measurements of the same tissue over time, and it can be used to interrogate lesions that are difficult or impossible to safely biopsy. Imaging can also capture heterogeneity within individual lesions and across all lesions in a patient. In addition, imaging can reflect the local in vivo microenvironment of the tumor in an unperturbed state as well as in response to therapy, and in ways not adequately represented by in vitro assays.

Pretreatment vitamin D level and response to neoadjuvant chemotherapy in women with breast cancer on the I-SPY trial (CALGB 150007/150015/ACRIN6657)

Laboratory studies suggest that vitamin D (vitD) enhances chemotherapy‐induced cell death. The objective of this study was to determine whether pretreatment vitD levels were associated with response to neoadjuvant chemotherapy (NACT) in women with breast cancer. Study patients (n = 82) were enrolled on the I‐SPY TRIAL, had HER2‐negative tumors, and available pretreatment serum. VitD levels were measured via DiaSorin radioimmunoassay. The primary outcome was pathologic residual cancer burden (RCB; dichotomized 0/1 vs. 2/3). Secondary outcomes included biomarkers of proliferation, differentiation, and apoptosis (Ki67, grade, Bcl2, respectively) and 3‐year relapse‐free survival (RFS). Mean and median vitD values were 22.7 ng/mL (SD 11.9) and 23.1 ng/mL, respectively; 72% of patients had levels deemed “insufficient” (<30 ng/mL) by the Institute of Medicine (IOM). VitD level was not associated with attaining RCB 0/1 after NACT (univariate odds ratio [OR], 1.01; 95% CI, 0.96–1.05) even after adjustment for hormone receptor status (HR), grade, Ki67, or body mass index (BMI). Lower vitD levels were associated with higher tumor Ki67 adjusting for race (OR, 0.95; 95% CI, 0.90–0.99). VitD level was not associated with 3‐year RFS, either alone (hazard ratio [HzR], 0.98; 95% CI, 0.95–1.02) or after adjustment for HR, grade, Ki‐67, BMI, or response. VitD insufficiency was common at the time of breast cancer diagnosis among women who were candidates for NACT and was associated with a more proliferative phenotype. However, vitD levels had no impact on tumor response to NACT or short‐term prognosis.

How Imaging Can Impact Clinical Trial Design: Molecular Imaging as a Biomarker for Targeted Cancer Therapy.

AbstractThe ability to measure biochemical and molecular processes to guide cancer treatment represents a potentially powerful tool for trials of targeted cancer therapy. These assays have traditionally been performed by analysis of tissue samples. However, more recently, functional and molecular imaging has been developed that is capable of in vivo assays of cancer biochemistry and molecular biology and is highly complementary to tissue-based assays. Cancer imaging biomarkers can play a key role in increasing the efficacy and efficiency of therapeutic clinical trials and also provide insight into the biologic mechanisms that bring about a therapeutic response. Future progress will depend on close collaboration between imaging scientists and cancer physicians and on public and commercial sponsors, to take full advantage of what imaging has to offer for clinical trials of targeted cancer therapy. This review will provide examples of how molecular imaging can inform targeted cancer clinical trials and clinical decision making by (1) measuring regional expression of the therapeutic target, (2) assessing early (pharmacodynamic) response to treatment, and (3) predicting therapeutic outcome. The review includes a discussion of basic principles of molecular imaging biomarkers in cancer, with an emphasis on those methods that have been tested in patients. We then review clinical trials designed to evaluate imaging tests as integrated markers embedded in a therapeutic clinical trial with the goal of validating the imaging tests as integral markers that can aid patient selection and direct response-adapted treatment strategies. Examples of recently completed multicenter trials using imaging biomarkers are highlighted.

Clinical Diagnosis and Management of Breast Cancer

The diagnosis and management of breast cancer are undergoing a paradigm shift from a one-size-fits-all approach to an era of personalized medicine. Sophisticated diagnostics, including molecular imaging and genomic expression profiles, enable improved tumor characterization. These diagnostics, combined with newer surgical techniques and radiation therapies, result in a collaborative multidisciplinary approach to minimizing recurrence and reducing treatment-associated morbidity. This article reviews the diagnosis and treatment of breast cancer, including screening, staging, and multidisciplinary management.