Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012

IMPORTANCE

Given recent advances in screening mammography and adjuvant therapy (treatment), quantifying their separate and combined effects on US breast cancer mortality reductions by molecular subtype could guide future decisions to reduce disease burden.

OBJECTIVE

To evaluate the contributions associated with screening and treatment to breast cancer mortality reductions by molecular subtype based on estrogen-receptor (ER) and human epidermal growth factor receptor 2 (ERBB2, formerly HER2 or HER2/neu).

DESIGN, SETTING, AND PARTICIPANTS

Six Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2012 using national data on plain-film and digital mammography patterns and performance, dissemination and efficacy of ER/ERBB2-specific treatment, and competing mortality. Multiple US birth cohorts were simulated.

EXPOSURES

Screening mammography and treatment.

MAIN OUTCOMES AND MEASURES

The models compared age-adjusted, overall, and ER/ERBB2-specific breast cancer mortality rates from 2000 to 2012 for women aged 30 to 79 years relative to the estimated mortality rate in the absence of screening and treatment (baseline rate); mortality reductions were apportioned to screening and treatment.

RESULTS

In 2000, the estimated reduction in overall breast cancer mortality rate was 37% (model range, 27%-42%) relative to the estimated baseline rate in 2000 of 64 deaths (model range, 56-73) per 100 000 women: 44% (model range, 35%-60%) of this reduction was associated with screening and 56% (model range, 40%-65%) with treatment. In 2012, the estimated reduction in overall breast cancer mortality rate was 49% (model range, 39%-58%) relative to the estimated baseline rate in 2012 of 63 deaths (model range, 54-73) per 100 000 women: 37% (model range, 26%-51%) of this reduction was associated with screening and 63% (model range, 49%-74%) with treatment. Of the 63% associated with treatment, 31% (model range, 22%-37%) was associated with chemotherapy, 27% (model range, 18%-36%) with hormone therapy, and 4% (model range, 1%-6%) with trastuzumab. The estimated relative contributions associated with screening vs treatment varied by molecular subtype: for ER+/ERBB2-, 36% (model range, 24%-50%) vs 64% (model range, 50%-76%); for ER+/ERBB2+, 31% (model range, 23%-41%) vs 69% (model range, 59%-77%); for ER-/ERBB2+, 40% (model range, 34%-47%) vs 60% (model range, 53%-66%); and for ER-/ERBB2-, 48% (model range, 38%-57%) vs 52% (model range, 44%-62%).

CONCLUSIONS AND RELEVANCE

In this simulation modeling study that projected trends in breast cancer mortality rates among US women, decreases in overall breast cancer mortality from 2000 to 2012 were associated with advances in screening and in adjuvant therapy, although the associations varied by breast cancer molecular subtype.

The life cycles of six multi-center adaptive clinical trials focused on neurological emergencies developed for the Advancing Regulatory Science initiative of the National Institutes of Health and US Food and Drug Administration: Case studies from the Adaptive Designs Accelerating Promising Treatments Into Trials Project

OBJECTIVES

Clinical trials are complicated, expensive, time-consuming, and frequently do not lead to discoveries that improve the health of patients with disease. Adaptive clinical trials have emerged as a methodology to provide more flexibility in design elements to better answer scientific questions regarding whether new treatments are efficacious. Limited observational data exist that describe the complex process of designing adaptive clinical trials. To address these issues, the Adaptive Designs Accelerating Promising Treatments Into Trials project developed six, tailored, flexible, adaptive, phase-III clinical trials for neurological emergencies, and investigators prospectively monitored and observed the processes. The objective of this work is to describe the adaptive design development process, the final design, and the current status of the adaptive trial designs that were developed.

METHODS

To observe and reflect upon the trial development process, we employed a rich, mixed methods evaluation that combined quantitative data from visual analog scale to assess attitudes about adaptive trials, along with in-depth qualitative data about the development process gathered from observations.

RESULTS

The Adaptive Designs Accelerating Promising Treatments Into Trials team developed six adaptive clinical trial designs. Across the six designs, 53 attitude surveys were completed at baseline and after the trial planning process completed. Compared to baseline, the participants believed significantly more strongly that the adaptive designs would be accepted by National Institutes of Health review panels and non-researcher clinicians. In addition, after the trial planning process, the participants more strongly believed that the adaptive design would meet the scientific and medical goals of the studies.

CONCLUSION

Introducing the adaptive design at early conceptualization proved critical to successful adoption and implementation of that trial. Involving key stakeholders from several scientific domains early in the process appears to be associated with improved attitudes towards adaptive designs over the life cycle of clinical trial development.

Association of Minimal Residual Disease With Clinical Outcome in Pediatric and Adult Acute Lymphoblastic Leukemia: A Meta-analysis

Importance

Minimal residual disease (MRD) refers to the presence of disease in cases deemed to be in complete remission by conventional pathologic analysis. Assessing the association of MRD status following induction therapy in patients with acute lymphoblastic leukemia (ALL) with relapse and mortality may improve the efficiency of clinical trials and accelerate drug development.

Objective

To quantify the relationships between event-free survival (EFS) and overall survival (OS) with MRD status in pediatric and adult ALL using publications of clinical trials and other databases.

Data Sources

Clinical studies in ALL identified via searches of PubMed, MEDLINE, and clinicaltrials.gov.

Study Selection

Our search and study screening process adhered to the PRISMA Guidelines. Studies that addressed EFS or OS by MRD status in patients with ALL were included; reviews, abstracts, and studies with fewer than 30 patients or insufficient MRD description were excluded.

Data Extraction and Synthesis

Study sample size, patient age, follow-up time, timing of MRD assessment (postinduction or consolidation), MRD detection method, phenotype/genotype (B cell, T cell, Philadelphia chromosome), and EFS and OS. Searches of PubMed and MEDLINE identified 566 articles. A parallel search on clinicaltrials.gov found 67 closed trials and 62 open trials as of 2014. Merging results of 2 independent searches and applying exclusions gave 39 publications in 3 arms of patient populations (adult, pediatric, and mixed). We performed separate meta-analyses for each of these 3 subpopulations.

Results

The 39 publications comprised 13 637 patients: 16 adult studies (2076 patients), 20 pediatric (11 249 patients), and 3 mixed (312 patients). The EFS hazard ratio (HR) for achieving MRD negativity is 0.23 (95% Bayesian credible interval [BCI] 0.18-0.28) for pediatric patients and 0.28 (95% BCI, 0.24-0.33) for adults. The respective HRs in OS are 0.28 (95% BCI, 0.19-0.41) and 0.28 (95% BCI, 0.20-0.39). The effect was similar across all subgroups and covariates.

Conclusions and Relevance

The value of having achieved MRD negativity is substantial in both pediatric and adult patients with ALL. These results are consistent across therapies, methods of and times of MRD assessment, cutoff levels, and disease subtypes. Minimal residual disease status warrants consideration as an early measure of disease response for evaluating new therapies, improving the efficiency of clinical trials, accelerating drug development, and for regulatory approval. A caveat is that an accelerated approval of a particular new drug using an intermediate end point, such as MRD, would require confirmation using traditional efficacy end points.

Abstract 3594: Adaptively randomized seamless-phase multiarm platform trial: Glioblastoma Multiforme Adaptive Global Innovative Learning Environment (GBM AGILE)

Traditional phase 3 clinical trials compare an experimental arm with control. They inefficiently use patients, time, and finances. Dramatic and rapid changes in biology makes such trials untenable. We describe an alternative drug development strategy that we are using in a particular setting, the trial GBM AGILE (Glioblastoma Multiforme Adaptive Global Innovative Learning Environment).

The trial’s design employs many innovations. Some aspects are similar to those of I-SPY 2 (see 4 articles in July 7, 2016 NEJM) but GBM AGILE extends I-SPY 2 in many ways. (1) It is a Bayesian platform trial that simultaneously evaluates many treatment arms (including combinations) from many companies. (2) Arms are added to the trial at any time and leave when they have been evaluated, whether positively or negatively. (3) An arm’s sample size is adaptive and based on frequent analyses of the trial results. (4) Every arm has an initial stage in which it is randomized adaptively: arms performing better in disease subtypes are assigned with higher probability to such patients. (5) An arm that performs sufficiently well in a disease subset moves seamlessly into a small (50-patient) confirmatory, registration stage in the same subset, with equal randomization against control. (6) All experimental arms are compared against a common control arm that is assigned to 20% of patients in every subtype; a bridging model takes advantage of having many arms in the trial and many comparisons among arms, and enables indirect randomization comparisons of all arms with all controls. (7) Patient subtypes are defined by line of therapy, MGMT methylation status for newly diagnosed patients, and biomarkers associated with targeted therapies, although adaptive randomization enables us to draw conclusions about off-target effects.

The many possible subtypes means that there are many possible drug indications. So there are many possible “error types” and no single definition of statistical power. For example, the trial may conclude that a drug’s indication is “recurrent, biomarker-positive” disease when in truth it is “all recurrent” disease. We show how the design addresses this issue and we define “pure type I error.”

GBM AGILE’s primary endpoint is overall survival (OS). To make the design more efficient we incorporate evaluations of patients’ statuses over time using a longitudinal model based on periodic MRI assessments and performance status. The longitudinal model and its components are not end points but rather provide auxiliary information that enables multiply imputing OS for surviving patients.

We represent the trial’s coordinating committees that are made up of more than 150 enthusiastic and devoted disease experts and advocates from around the globe, including from Australia and China. The U.S. FDA has been enormously helpful in designing GBM AGILE, especially as regards its potential for drug and biomarker registration.

Our approach provides a model for other diseases, including those outside of cancer.

Citation Format: Donald A. Berry, Todd Graves, Jason Connor, Brian Alexander, Timothy Cloughesy, Anna Barker, Scott M. Berry, for the GBM AGILE Global Alliance. Adaptively randomized seamless-phase multiarm platform trial: Glioblastoma Multiforme Adaptive Global Innovative Learning Environment (GBM AGILE) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3594. doi:10.1158/1538-7445.AM2017-3594

Pembrolizumab plus standard neoadjuvant therapy for high-risk breast cancer (BC): Results from I-SPY 2

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Background: Pembro is an anti-PD-1 antibody with single agent activity in HER2– metastatic BC. I-SPY 2 is a multicenter, phase 2 platform trial which evaluates novel neoadjuvant therapies; the primary endpoint is pathological complete response (pCR, ypT0/Tis ypN0). We report current efficacy results, with final results at ASCO. Methods: Patients (pts) with invasive BC ≥2.5 cm by exam or ≥2 cm by imaging are assigned weekly paclitaxel x 12 (control) +/- an experimental agent, followed by doxorubicin/cyclophosphamide x 4. Combinations of hormone-receptor (HR), HER2, & MammaPrint (MP) status define the 8 signatures studied. MP low HR+ BC is excluded. Adaptive randomization is based on each arm’s Bayesian probability of superiority over control. Graduation by signature is based on an arm’s Bayesian predictive probability of a successful 1:1 randomized phase 3 trial with a pCR endpoint. We provide raw & Bayesian estimated pCR rates adjusted for covariates, time effects over the course of the trial, & serial MRI modeling for pts not yet assessed for pCR surgically. Results: 69 pts were randomized to pembro (HER2- subsets only) from Dec 2015 until it graduated in Nov 2016. 46 pts have undergone surgery (table); the other 23 have on-therapy MRI assessments. In 29 HR–/HER2– (TNBC) pts, pembro increased raw & estimated pCR rates by >50% & 40%, respectively; in 40 HR+/HER– pts, it did so by 13% and 21%. 5 pts had immune-related grade 3 adverse events (AEs); 1 hypophysitis & 4 adrenal insufficiency. 4 pts presented after completion of AC (149-179 d after starting pembro); 1 presented prior to AC (37 d after starting pembro). 7 pts had grade 1-2 thyroid abnormalities. Conclusion: Pembro added to standard therapy improved pCR rates in all HER2- BCs that meet I-SPY 2 eligibility, especially in TNBC. Immune-mediated AEs were observed; pt follow up is ongoing. Clinical trial information: NCT01042379. [Table: see text]

Creating a synthetic control arm from previous clinical trials: Application to establishing early end points as indicators of overall survival in acute myeloid leukemia (AML)

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Background: Clinical trials of experimental drugs require controls. Concurrently randomized controls are the gold standard for judging drug effect. Historical controls are not ideal but are much more efficient and economical. Historical controls derived from a single clinical trial have the biases of that trial. Using many trials with comparable end points and eligibility minimizes such bias. Medidata’s archive contains >3000 trials with clinical data rights for deidentified aggregated analyses. We used this resource to develop a synthetic control arm (SCA) for a particular phase I/II single-arm trial in AML. We demonstrate the utility of this approach by addressing a different but equally important issue: establishing early end points as predictors of long term clinical outcomes. Methods: We built an SCA from 7 relapsed/refractory AML trials completed in last 5 yrs. They had similar eligibility criteria as a particular phase I/II trial for an investigational agent. We selected subjects for the SCA who had baseline covariates matching the subjects in the tri.al. Data cleaning and standardization ensured consistency of data fields. The primary outcomes were CR (complete remission) and CRi (CR without hematologic recovery) at 56 days, and overall survival (OS) subsequent to 56 days. Non-CR/non-CRi deaths before 56 days were set to OS=0. We used a landmark analysis to correlate CR and CRi with OS, calculating the hazard ratio (HR) of OS of CR and CRi vs its comparison group. Results: The SCA included 340 subjects (median age 63 yrs, 55% male, 77% White Non-Hispanic, 28% ECOG 0). Results are in this table. Conclusions: The Medidata trial archive is a resource for creating SCAs. The example SCA we created identified well-defined subjects for whom a CR or CRi is associated with longer OS. Investigations of SCAs for other drugs could aid in addressing the types of subjects and drug categories for which CR or CR/CRi predict longer OS. Such information can help build more efficient and more informative adaptive clinical trials. [Table: see text]

Abstract P6-11-04: The evaluation of ganitumab/metformin plus standard neoadjuvant therapy in high-risk breast cancer: Results from the I-SPY 2 trial

Background: I-SPY 2 is a multicenter, phase 2 trial using response-adaptive randomization within biomarker subtypes to evaluate novel agents when added to standard neoadjuvant therapy for women with high-risk stage II/III breast cancer - investigational agent(I) +paclitaxel(T) qwk, doxorubicin & cyclophosphamide(AC) q2-3 wk x 4 vs. T/AC (control arm). The primary endpoint is pathologic complete response (pCR) at surgery. The goal is to identify/graduate regimens that have ≥85% Bayesian predictive probability of success (statistical significance) in a 300-patient phase 3 neoadjuvant trial defined by hormone-receptor (HR) & HER2 status & MammaPrint (MP). Regimens may also leave the trial for futility (< 10% probability of success) or following accrual of maximum sample size (10%< probability of success <85%). We report the results for experimental arm Ganitumab, a type I insulin-like growth factor receptor (IGF1R) inhibitor. IGF1R inhibitors are known to induce insulin resistance and all patients assigned to Ganitumab received metformin.

Methods: Women with tumors ≥2.5cm were eligible for screening. MP low/HR+ and HER2+ tumors were ineligible for randomization. Hemoglobin A1C≥ 8.0% were ineligible. MRI scans (baseline, 3 cycles after start of therapy, at completion of weekly T and prior to surgery) were used in a longitudinal statistical model to improve the efficiency of adaptive randomization. Ganitumab was given at 12mg/kg q2 weeks and metformin at 850mg PO BID, while receiving ganitumab. Analysis was intention to treat with patients who switched to non-protocol therapy counted as non-pCRs. Ganitumab/metformin was open only to HER2- patients, and eligible for graduation in 3 of 10 pre-defined signatures: HER2-, HR+HER2- and HR-HER2-.

Results: Ganitumab/metformin did not meet the criteria for graduation in the 3 signatures tested. When the maximum sample size was reached, accrual to this arm stopped. Ganitumab/metformin was assigned to 106 patients; there were 128 controls. We report probabilities of superiority for Ganitumab/metformin over control and Bayesian predictive probabilities of success in a neoadjuvant phase 3 trial equally randomized between Ganitumab/metformin and control, for each of the 3 biomarker signatures, using the final pathological response data from all patients. Safety data will be presented.

SignatureEstimated pCR Rate (95% probability interval)Probability Ganitumab/ Metformin Is Superior to ControlPredictive Probability of Success in Phase 3 Ganitumab/ Metformin N = 106Control N = 128  All HER2-22% (13%-31%)16% (10%-23%)89%33%HR+/HER2-14% (4%-24%)12% (4%-19%)66%21%HR-/HER2-32% (17%-46%)21% (11%-32%)91%51%

Conclusion: The I-SPY 2 adaptive randomization study estimates the probability that investigational regimens will be successful in a phase 3 neoadjuvant trial. The value of I-SPY 2 is to give insight about the performance of an investigational agent's likelihood of achieving pCR. For Ganitumab/metformin, no subtype came close to the efficacy threshold of 85% likelihood of success in phase 3, and this regimen does not appear to impact upfront reduction of tumor burden. Our data do not support its continued development for the neoadjuvant treatment of breast cancer.

Citation Format: Yee D, Paoloni M, van't Veer L, Sanil A, Yau C, Forero A, Chien AJ, Wallace AM, Moulder S, Albain KS, Kaplan HG, Elias AD, Haley BB, Boughey JC, Kemmer KA, Korde LA, Isaacs C, Minton S, Nanda R, DeMichele A, Lang JE, Buxton MB, Hylton NM, Symmans WF, Lyandres J, Hogarth M, Perlmutter J, Esserman LJ, Berry DA. The evaluation of ganitumab/metformin plus standard neoadjuvant therapy in high-risk breast cancer: Results from the I-SPY 2 trial [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-11-04.

Abstract P6-11-02: Efficacy of Hsp90 inhibitor ganetespib plus standard neoadjuvant therapy in high-risk breast cancer: Results from the I-SPY 2 trial

Background:Pathologic complete response(pCR) after neoadjuvant therapy is an established prognostic biomarker for high-risk breast cancer(BC). Improving pCR rates may identify new therapies that improve survival. I-SPY 2 uses response-adaptive randomization within biomarker subtypes to evaluate novel agents when added to standard neoadjuvant therapy for women with high-risk stage II/III breast cancer; the goal is to identify regimens that have ≥85% Bayesian predictive probability of success (statistical significance) in a 300-patient phase 3 neoadjuvant trial defined by hormone-receptor (HR), HER2 status and MammaPrint (MP). We report the results for Ganetespib, a selective inhibitor of Hsp90 that induces the degradation/deactivation of key drivers of tumor initiation, progression, angiogenesis, and metastasis.Ganetespib + taxanes previously have resulted in a superior therapeutic response compared to monotherapy in multiple solid tumor models including BC.

Methods:Women with tumors ≥2.5cm were eligible for screening and participation. MP low/HR+ tumors were ineligible for randomization. QTcF >470msec and HbA1C >8.0% were ineligible. MRI scans (baseline, +3 cycles, following weekly paclitaxel, T, and pre-surgery) were used in a longitudinal statistical model to improve the efficiency of adaptive randomization. Ganetespib was given with weekly T at 150 mg/m2 IV weekly (3 weeks on, 1 off). Patients were premedicated (dexamethasone 10mg and diphenhydramine HCl 25-50 mg, or therapeutic equivalents). Analysis was intention to treat with patients who switched to non-protocol therapy counted as non-pCRs. The Ganetespib regimen was open only to HER2- patients, and eligible for graduation in 3 of 10 pre-defined signatures: HER2-, HR+/HER2- and HR-/HER2-.

Results:Ganetespib did not meet the criteria for graduation in the 3 signatures tested. When the maximum sample size was reached, accrual stopped. Ganetespib was assigned to 93 patients; there were 140 controls. We report probabilities of superiority for Ganetespib over control and Bayesian predictive probabilities of success in a neoadjuvant phase 3 trial equally randomized between Ganetespib and control, for the 3 biomarker signatures, using the final pCR data from all patients. Safety data will be presented.

SignatureEstimated pCR Rate (95% probability interval)Probability Ganetespib Is Superior to ControlPredictive Probability of Ganetespib Success in a Phase 3 Trial Ganetespib N = 93Control N = 140  All HER2-26% (16%-37%)18% (8%-28%)91%47%HR+/HER2-15% (4%-27%)14% (4%-24%)60%19%HR-/HER2-38% (23%-53%)22% (9%-35%)96%72%

Conclusion:The I-SPY 2 adaptive randomization model efficiently evaluates investigational agents in the setting of neoadjuvant BC. The value of I-SPY 2 is that it provides insight as to the regimen's likelihood of success in a phase 3 neoadjuvant study. Although no signature reached the efficacy threshold of 85% likelihood of success in phase 3, we observed the most impact in HR-/HER2- patients, with a 16% improvement in pCR rate. While our data do not support the continued development of Ganetespib alone for neoadjuvant BC, combinations with Ganetespib, which could potentiate its effect, may be worth pursuing in I-SPY 2 or similar trials.

Citation Format: Forero A, Yee D, Buxton MB, Symmans WF, Chien AJ, Boughey JC, Elias AD, DeMichele A, Moulder S, Minton S, Kaplan HG, Albain KS, Wallace AM, Haley BB, Isaacs C, Korde LA, Nanda R, Lang JE, Kemmer KA, Hylton NM, Paoloni M, van't Veer L, Lyandres J, Perlmutter J, Hogarth M, Yau C, Sanil A, Berry DA, Esserman LJ. Efficacy of Hsp90 inhibitor ganetespib plus standard neoadjuvant therapy in high-risk breast cancer: Results from the I-SPY 2 trial [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-11-02.

Abstract P2-11-02: A longitudinal look at toxicity management within a platform trial: Lessons from the I-SPY 2 TRIAL

Background: I-SPY 2 is a multicenter, phase 2 trial using response-adaptive randomization within biomarker subtypes to evaluate a series of investigational agents or regimens when added to standard neoadjuvant therapy for women with high-risk stage II/III breast cancer - investigational agent (I) +paclitaxel (T) qwk, doxorubicin & cyclophosphamide (AC) q2-3 wk x 4 vs. T+/-HP/AC (control arm(s)). Although the primary endpoint is pathologic complete response (pCR) at surgery, a key secondary aim is to evaluate the toxicity profiles of these investigational agents. Distinct aspects of safety monitoring in a platform trial, as well as the specificities of safety management in a potentially curative population make the experiences from I-SPY 2 valuable to the community.

Methods: Inclusion and exclusion criteria are uniformly applied to all women in I-SPY 2. When a new investigational agent/regimen is planned for the trial, agent specific laboratory/hematologic limits or additional required tests are added, as needed. Eligibility criteria remain in the trial for its duration and apply to all investigational and control arms. Laboratory and adverse event data are collected and monitored in real time. The lead investigator of the investigational agent/regimen who chaperones a specific agent/regimen through the trial (“Agent Chaperone”), Medical Monitor, I-SPY 2 Agents Committee, CRO safety group, and an active DSMB that meets monthly oversee the management of toxicities within each investigational agent/regimen of the trial. Toxicity profiles for an investigational agent/regimen are compared to their relevant control. Safety analyses are intention to treat.

Results: From March 2010-May 2016, eleven (11) investigational agents/regimens have opened (and 6 have completed evaluation) and 973 women have been randomized. These agents/regimens span a variety of mechanisms of action including targeted therapies such as small molecule inhibitors and antibodies, as well as immunotherapies. Additions to the trial's eligibility criteria have been made with new investigational arms. Adverse events of special interest have been monitored for each investigational arm and specific toxicities treated uniformly when applicable. A risk-based monitoring plan has been implemented that focuses on the collection and review of the trial's most critical data elements including serious adverse events and drug specific safety issues, allowing for a more efficient and focused effort. Safety issues have been quickly addressed and requirements updated, when needed, given the importance of limiting (or avoiding) long-term safety complications within this neoadjuvant patient population. Accrual to the trial has (been) maintained over time and the safety of trial participants has been well managed.

Conclusion: A platform trial requires an evolving, and focused safety-monitoring process that adapts as new investigational agents are included. I-SPY 2's infrastructure and team science approach has created a system to manage patients across multiple arms with different risk profiles. These practices will support the safe evaluation of additional new combinations and regimens and serves as a guide for safety management within standing platform trials.

Citation Format: Paoloni M, Lyandres J, Buxton MB, Berry DA, Esserman LJ, DeMichele A, Yee D. A longitudinal look at toxicity management within a platform trial: Lessons from the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P2-11-02.

Abstract S3-05: Integrated analysis of multidimensional genomic data on CALGB 40601 (Alliance), a randomized neoadjuvant phase III trial of weekly paclitaxel (T) and trastuzumab (H) with or without lapatinib (L) for HER2-positive breast cancer

Background: RNA profiling and mutational analyses in CALGB 40601 (NCT00770809) found significant impact on pathologic complete response (pCR) rates from tumor (intrinsic subtype, p53 mutation) and microenvironmental (immune cell) features. Integrated analysis across platforms is needed to better understand the roles of these different factors with respect to response to HER2-targeted therapies.

Methods: We performed a comprehensive genomic analyses on pCR, defined as no invasive tumor in the breast, by integrating clinicopathological information with somatic mutation status, 422 segment-level DNA Copy Number Alterations (CNAs), and 510 gene expression signatures using mRNAseq and DNA exome sequencing from 213 pre-treatment tumors. Excluding 48 samples in the TL arm that was closed early due to futility, and 4 Normal-like tumors, the dataset consisted of 161 patients from TH and THL arms including 47 HER2-enriched (HER2E), 8 Basal-like, 54 Luminal A, and 52 Luminal B, all of whom received H. The main analysis was performed using the Elastic Net on multivariate logistic regression models for predicting pCR. The samples were divided into a training and a test set, then models were built to predict pCR by 10-fold cross-validation in the training set, then applying the best model onto the test set to construct ROC curves and evaluate prediction accuracy by calculating area under ROC (AUC). We also used the DawnRank, a network-based bioinformatics tool that integrates DNA and RNA data to identify driver genes, to find predictors of resistance to H-containing therapies.

Results: Among clinicopathological factors, clinical estrogen/progesterone receptor (ER/PgR) status and intrinsic subtype by PAM50 were statistically associated with pCR, but treatment arm (TH vs THL) and stage were not. In the Elastic Net analysis, the models incorporating either gene signatures (AUC: 0.724) or CNAs (AUC: 0.777) were more predictive of response than mutation status model (AUC: 0.635). Gene signatures and CNAs were further combined with either mutation status (AUC: 0.773), clinical ER/PgR status (AUC: 0.787) or ER/PgR status plus intrinsic subtype (AUC: 0.784). The combination with the highest AUC comprised gene signatures, CNAs, and ER/PgR status, and demonstrated that CNAs at Chromosome (Chr.) 6p, 10q22, or 11q23, the signature of Correlation to HER2E, and a T-cell signature, positively predicted pCR and that Luminal and PgR gene signatures were negative predictors. The CN gain of Chr.6p, which contains the HLA genes, predicted for pCR and was associated with higher expression of HLA genes and B cell / IgG signatures. The CN loss of Chr.11q23 including CD3D, CD3E, and CD3G was also identified by DawnRank as a region associated with resistance.

Conclusions: Tumor genetics (CNAs), tumor RNA subtype (HER2E, Luminal), and the microenvironment (immune cells) were independently predictive of response to H-containing therapies and biologically and clinically important for HER2-positive breast cancer, supporting integrated RNA- and DNA-based tumor assessments to clarify response to HER2-targeting.

Support: U10CA031946/033601/180821/180882/180888.

Citation Format: Tanioka M, Fan C, Carey LA, Hyslop T, Pitcher BN, Parker JA, Hoadley KA, Henry NL, Tolaney S, Dang C, Krop IE, Harris L, Berry DA, Mardis E, Perou CM, Winer EP, Hudis CA. Integrated analysis of multidimensional genomic data on CALGB 40601 (Alliance), a randomized neoadjuvant phase III trial of weekly paclitaxel (T) and trastuzumab (H) with or without lapatinib (L) for HER2-positive breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr S3-05.

Abstract PD1-03: Multivariate analysis of subtype and gene expression signatures predictive of pathologic complete response (pCR) in triple-negative breast cancer (TNBC): CALGB 40603 (Alliance)

Support: U10CA180821, U10CA180882

Background: The addition of either carboplatin (Cb) or bevacizumab (Bev) to standard neoadjuvant chemotherapy (NACT) increases pCR rates in TNBC overall and in the dominant subset of basal-like cancers (Sikov et al, JCO 2015; Sikov et al, SABCS 2014). Multigene expression signatures more accurately reflect tumor biology for response prediction and prognosis than individual gene expression. We evaluated the ability of multivariate analysis of gene expression signatures to create predictive models for achievement of pCR in TNBC.

Methods: RNA sequencing was successful on 389 pretreatment samples from patients with available pCR data, and used to assign PAM50 subtype and calculate gene signatures scores for 489 published expression signatures. Elastic net, a penalized regression model for high dimensional variable selection, was used to select features associated with pCR in all TNBC and in the basal-like subset. Models were derived in a training set (2/3 of samples) and validated in a separate test set (1/3). A separate model was derived using 196 TNBC samples from patients treated only on the standard NACT +/- Cb arms for application to external TNBC neoadjuvant data sets not treated with Bev.

Results: Consistent with our prior partial data set, 343 (88%) of the cancers were classified basal-like, in whom the in breast pCR rate was 54%; the remainder were classified normal-like (n=32) or HER2-enriched (n=14) with a non-basal pCR rate of 56%. Elastic Net analysis in all TNBC generated a model of 23 signatures and treatment assignment with 68% sensitivity and 64% specificity. The area under the curve was 0.64 (p-value=0.0019). Nineteen modules, including immune cell signatures (Th1, NK, IgG), immunoglobulin variable region expression, addition of Cb and Bev and expression of genes at regions 15q25, 17p11.2-13.3, and 8p22 were positively associated with response. The latter two regions are associated with aggressive breast cancer, and while not part of the 17p13 signature, this region contains TP53, a gene important in TNBC. Six modules were associated with resistance, including luminal progenitor, TGFB, NOTCH, FOS/JUN, 8p amplicon, and eosinophil signatures. When limited to basal-like samples, a model including 32 modules and addition of Cb and Bev was generated, with 62.3% sensitivity and 59.1% specificity. Seventeen features were selected in both models. Omitting Bev-treated patients, a model using just the gene expression signatures was developed. The predictive value of this model will be assessed using an external cohort of TNBC patients treated with neoadjuvant docetaxel and Cb (NCT01560663) and results presented.

Conclusions: Multivariate analysis of gene expression signatures derived from pretreatment samples enabled the construction of models to predict achievement of pCR in TNBC. These models performed well on our test set, and will be assessed for their predictive ability in other TNBC data sets. If validated by future analyses, this could help us identify patients likely to achieve pCR with standard NACT and may benefit from the addition of agents such as Cb or Bev.

ClinicalTrials.gov Identifier: NCT00861705.

Citation Format: Hoadley KA, Hyslop T, Fan C, Berry DA, Hahn O, Tolaney SM, Sikov WM, Perou CM, Carey LA. Multivariate analysis of subtype and gene expression signatures predictive of pathologic complete response (pCR) in triple-negative breast cancer (TNBC): CALGB 40603 (Alliance) [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr PD1-03.

Abstract P5-11-18: Trajectory of patient (Pt) reported physical function (PF) during and after neoadjuvant chemotherapy in the I-SPY 2 trial

Background

Patients (pts) receiving chemotherapy for breast cancer experience toxicities impacting short and long-term quality of life (QOL). Within I-SPY 2, a trial adaptively randomizing stage II/III breast cancer pts to neoadjuvant chemotherapy +/- an investigational agent, we are collecting pt reported outcome (PRO) data to understand the impact of investigational agents on QOL. This PRO sub-study provides a unique opportunity to study QOL longitudinally and explore how pt and tumor characteristics, exposure to investigational therapies, and surgical outcome impact QOL.

Methods

Pts enrolled in this trial receive paclitaxel (T) +/- an investigational agent for 12 weeks followed by 4 cycles of doxorubicin and cyclophosphamide (AC). Surveys include the EORTC QLQ-C30 and BR-23, and PROMIS measures for QOL metrics including but not limited to physical function (PF), anxiety, and depression. Surveys are administered pre-chemotherapy to 2 years post-surgery. PF data from the EORTC and PROMIS instruments was analyzed for 238 pts at 5 sites (UCSF, UCSD, U of Pennsylvania, U of Minnesota, and Swedish Cancer Center). 48 pts completed baseline, inter-regimen (between T and AC), pre-operative and post-surgery surveys. Of the 48 pts 32 completed a 6-month follow up (FUP) and 31 completed a 1-year FUP survey. A linear mixed effect model, adjusting for HER2 status and treatment type was used to evaluate changes in PF over time. Sample size is small and statistics are descriptive rather than inferential.

Results

Median age of pts in this analysis was 50 (range 27-72).

Table 1 shows PROMIS & EORTC PF scores in this cohort.Time Point  PROMISEORTC  nMeanSEMeanSEPre-TreatmentAll4852.51.092.02.0 HER2+1553.51.594.12.2 HER2-3352.11.391.12.8Inter-RegimenAll4845.51.282.22.7 HER2+1548.62.384.44.2 HER2-3344.11.381.23.4Pre-SurgeryAll4843.91.179.42.3 HER2+1545.12.275.34.1 HER2-3343.41.381.32.86-Month FUPAll3248.11.487.41.9 HER2+1247.52.285.03.3 HER2-2048.41.888.92.41 Year FUPAll3148.91.488.43.1 HER2+949.12.988.95.4 HER2-2248.81.788.33.8

At baseline, mean PROMIS PF scores were higher than the US average (mean = 50) but declined as expected throughout treatment. HER2+ patients experienced a similar degree of recovery as HER2- pts post-surgery despite adjuvant treatment with Herceptin. Analysis of post-operative PROMIS PF indicated an average score within the U.S. general population (mean =50) but did not return to higher functioning seen at baseline levels (mean 52.5, p-value < 0.05). Analysis of the EORTC PF sub-scale demonstrated a similar trend; however, the baseline and post-operative difference was not significant (p-value=0.15 for both FUP). Finding supports PROMIS PF ability to measure high functioning cancer patients.

Conclusions: Among a subset of pts who completed all surveys in the I-SPY 2 QOL substudy, PF did not return to baseline at 6-12 months post-operatively. Through transition to an electronic platform of data collection we hope to improve compliance with survey completion. We continue to analyze other QOL measures and plan to correlate QOL data with treatment arm, adverse events, comorbidities, and response to neoadjuvant treatment.

Citation Format: Shah M, Jensen R, Yau C, Straehley I, Berry DA, DeMichele A, Buxton MB, Hylton NM, Perlmutter J, Symmans WF, Tripathy D, Yee D, Wallace A, Kaplan HG, Clark A, Chien AJ, I-SPY 2 Investigators, Esserman LJ, Melisko ME. Trajectory of patient (Pt) reported physical function (PF) during and after neoadjuvant chemotherapy in the I-SPY 2 trial [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-11-18.

A conceptual model for the development process of confirmatory adaptive clinical trials within an emergency research network

BACKGROUND

Adaptive clinical trials use accumulating data from enrolled subjects to alter trial conduct in pre-specified ways based on quantitative decision rules. In this research, we sought to characterize the perspectives of key stakeholders during the development process of confirmatory-phase adaptive clinical trials within an emergency clinical trials network and to build a model to guide future development of adaptive clinical trials.

METHODS

We used an ethnographic, qualitative approach to evaluate key stakeholders' views about the adaptive clinical trial development process. Stakeholders participated in a series of multidisciplinary meetings during the development of five adaptive clinical trials and completed a Strengths-Weaknesses-Opportunities-Threats questionnaire. In the analysis, we elucidated overarching themes across the stakeholders' responses to develop a conceptual model.

RESULTS

Four major overarching themes emerged during the analysis of stakeholders' responses to questioning: the perceived statistical complexity of adaptive clinical trials and the roles of collaboration, communication, and time during the development process. Frequent and open communication and collaboration were viewed by stakeholders as critical during the development process, as were the careful management of time and logistical issues related to the complexity of planning adaptive clinical trials.

CONCLUSION

The Adaptive Design Development Model illustrates how statistical complexity, time, communication, and collaboration are moderating factors in the adaptive design development process. The intensity and iterative nature of this process underscores the need for funding mechanisms for the development of novel trial proposals in academic settings.

Axillary Management of Stage II/III Breast Cancer in Patients Treated with Neoadjuvant Systemic Therapy: Results of CALGB 40601 (HER2-Positive) and CALGB 40603 (Triple-Negative)

BACKGROUND

Management of the axilla in stage II/III breast cancer undergoing neoadjuvant systemic therapy (NST) is controversial. To understand current patterns of care, we collected axillary data from 2 NST trials: HER2-positive (Cancer and Leukemia Group B [CALGB] 40601) and triple-negative (CALGB 40603).

STUDY DESIGN

Axillary evaluation pre- and post-NST was per the treating surgeon and could include sentinel node biopsy. Post-NST, node-positive patients were recommended to undergo axillary lymph node dissection (ALND). We report pre-NST histopathologic nodal evaluation and post-NST axillary surgical procedures with correlation to clinical and pathologic nodal status.

RESULTS

Seven hundred and forty-two patients were treated, 704 had complete nodal data pre-NST and post-NST. Pre-NST, 422 (60%) of 704 patients underwent at least 1 procedure for axillary node evaluation (total of 468 procedures): fine needle aspiration (n = 234; 74% positive), core needle biopsy (n = 138; 72% positive), and sentinel node biopsy (n = 96; 33% positive). Pre-NST, 304 patients were considered node-positive. Post-NST, 304 of 704 patients (43%) underwent sentinel node biopsy; 44 were positive and 259 were negative (29 and 36 patients, respectively, had subsequent ALND). Three hundred and ninety-one (56%) patients went directly to post-NST ALND and 9 (1%) pre-NST node-positive patients had no post-NST axillary procedure. Post-NST, 170 (24%) of the 704 patients had residual axillary disease. Agreement between post-NST clinical and radiologic staging and post-NST histologic staging was strongest for node-negative (81%) and weaker for node-positive (N1 31%, N2 29%), with more than half of the clinically node-positive patients found to be pathologic negative (p < 0.001).

CONCLUSIONS

Our results suggest there is no widely accepted standard for axillary nodal evaluation pre-NST. Post-NST staging was highly concordant in patients with N0 disease, but poorly so in node-positive disease. Accurate methods are needed to identify post-NST patients without residual axillary disease to potentially spare ALND.

Risk of acute myeloid leukemia and myelodysplastic syndrome among older women receiving anthracycline-based adjuvant chemotherapy for breast cancer on Modern Cooperative Group Trials (Alliance A151511)

PURPOSE

We examined acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) events among 9679 women treated for breast cancer on four adjuvant Alliance for Clinical Trials in Oncology trials with >90 months of follow-up in order to better characterize the risk for AML/MDS in older patients receiving anthracyclines.

METHODS

We used multivariable Cox regression to examine factors associated with AML/MDS, adjusting for age (≥65 vs. <65 years; separately for ≥70 vs. <70 years), race/ethnicity, insurance, performance status, and anthracycline receipt. We also examined the effect of cyclophosphamide, the interaction of anthracycline and age, and outcomes for those developing AML/MDS.

RESULTS

On Cancer and Leukemia Group B (CALGB) 40101, 49907, 9344, and 9741, 7290 received anthracyclines; 15% were in the age ≥65 and 7% were ≥70. Overall, 47 patients developed AML/MDS (30 AML [0.3%], 17 MDS [0.2%]); 83% of events occurred within 5 years of study registration. Among those age ≥65 and ≥70, 0.8 and 1.0% developed AML/MDS (vs. 0.4% for age <65), respectively. In adjusted analyses, older age and anthracycline receipt were significantly associated with AML/MDS (adjusted hazard ratio [HR] for age ≥65 [vs. <65] = 3.13, 95% confidence interval [CI] 1.18-8.33; HR for anthracycline receipt [vs. no anthracycline] = 5.16, 95% CI 1.47-18.19). There was no interaction between age and anthracycline use. Deaths occurred in 70% of those developing AML/MDS.

CONCLUSIONS

We observed an increased risk for AML/MDS for older patients and those receiving anthracyclines, though these events were rare. Our results help inform discussions surrounding anticipated toxicities of adjuvant chemotherapy in older patients.

Impact of neoadjuvant therapy on eligibility for and frequency of breast conservation in stage II-III HER2-positive breast cancer: surgical results of CALGB 40601 (Alliance)

OBJECTIVE

It had been previously shown that patients who receive neoadjuvant systemic therapy (NST) are more likely to undergo breast-conserving therapy (BCT) than those who have primary surgery. However, the frequency with which patients who are not BCT-eligible prior to NST convert to BCT-eligible with treatment is unknown. To document this conversion rate in a subset of patients expected to have a high clinical response rate to NST, we studied surgical assessment and management of patients enrolled on a randomized neoadjuvant trial for stage II-III HER2-positive breast cancer (HER2 + BC)(CALGB 40601).

METHODS

The treating surgeon assessed BCT candidacy based on clinico-radiographic criteria both before and after NST. Definitive breast surgical management was at surgeon and patient discretion. We sought to determine (1) the conversion rate from BCT-ineligible to BCT-eligible (2) the percentage of BCT-eligible patients who chose breast conservation, and (3) the rate of successful BCT. We also evaluated surgeon-determined factors for BCT-ineligibility and the correlation between BCT eligibility and pathologic complete response (pCR).

RESULTS

Of 292 patients with pre- and post-NST surgical assessments, 59 % were non-BCT candidates at baseline. Of the 43 % of these patients who converted with NST, 67 % opted for BCT, with an 80 % success rate. NST increased the BCT-eligible rate from 41 to 64 %. Common factors cited for BCT-ineligibility prior to NST including tumor size (56 %) and probable poor cosmetic outcome (26 %) were reduced by 67 and 75 %, respectively, with treatment, while multicentricity, the second most common factor (33 %), fell by only 16 %. Since 23 % of the BCT-eligible patients chose mastectomy, BCT was the final surgical procedure in just 40 % of the patients. Patients considered BCT-eligible both at baseline and after NST had a pCR rate of 55 %, while patients who were BCT-ineligible prior to NST had the same pCR rate (44 %) whether they converted to BCT-eligible or not.

CONCLUSIONS

Many patients with HER2 + BC deemed ineligible for BCT at baseline can be converted to BCT-eligible with NST; excluding patients with multicentric disease substantially increases that percentage. In converted patients who opt for BCT, the success rate is similar to that of patients considered BCT-eligible at baseline. Whether a BCT-ineligible patient converts to BCT eligibility or not does not appear to affect the likelihood of achieving a pCR. Despite the efficacy of NST in this patient cohort, only 40 % of patients had successful BCT; further research into why BCT-eligible patients often opt for mastectomy is needed.

Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis

The Bayesian approach and several of its advantages in drug and medical device development are described. One advantage from the perspective of analysis is that it provides a methodology for synthesizing information. However, taking a Bayesian approach to designing clinical trials is potentially more valuable than using this approach in analyzing trial results. Bayesian methodology provides a mechanism for updating what is known as results accumulate during a trial. Such updating can be incorporated completely explicitly and prospectively. An important way in which the Bayesian approach can be used is in calculating the predictive probability distribution of future results on the basis of current results. I show how to exploit predictive distributions in adapting to results that accumulate during the course of a trial. Possible adaptations including decreasing or increasing sample size, dropping treatment arms, and modifying the randomization proportions to the various arms depending on the interim results. Consequences of taking a Bayesian approach to clinical trial design are efficiency, better treatment of patients in the trial, and greater precision regarding the primary endpoints. An example of the last of these is Bayesian modeling of the relationship between early and longer term endpoints. Such modeling also enables earlier decision making. Case studies 2 and 3 deal with trials that were shorter and smaller, respectively, because of such modeling.

Bayesian predictions of final outcomes: regulatory approval of a spinal implant

We describe a randomized controlled trial of an investigational spinal implant. The investigational device has an obvious benefit in comparison with control in that it precludes the need for harvesting bone graft and the pain and morbidity associated with it. Therefore, the principal comparison is one of noninferiority. The primary endpoint is overall success at two years. The “noninferiority margin” is 10%. Waiting for two years after the last patient's surgery may not be necessary depending on earlier measurements of success. We model the relationship between one-and two-year results. Our Bayesian analysis considers all available information, including some patients who have both one-and two-year results and some patients who have only one-year results. Our study provides an example in which Bayesian predictive modeling provided earlier information than otherwise and therefore it shortened the time line of the development of a therapeutic strategy.

Advancing Clinical Trials to Streamline Drug Development

The last decade in oncology has been marked by the identification of numerous new potential cancer targets and even more agents designed to inhibit them. The matrix of new targets, new agents, and the companion diagnostics required to identify the right patient for the right drug has created a major challenge for the clinical trial process. This has been compounded by the addition of new immunomodulators targeting the host immune system rather than the tumor. Recognizing the need for new approaches, industry, investigators, and regulators have responded to this challenge. New clinical trial designs are being evaluated to incorporate the genomic sequence data being obtained almost routinely after cancer diagnosis. New dose-finding approaches are being proposed to identify the maximum effective dose rather than the maximum tolerated dose. The FDA is involved in the drug approval process from points early in development and has accepted registration quality data from expansion cohorts in support of drug approval. Despite progress on several fronts, many challenges remain, including the lack of predictability of preclinical data for clinical results and phase II data for phase III results, an infrastructure that can be an obstacle to clinical trial development and implementation, and the increasing use of contracted clinical research organizations that limit a fit-for-purpose approach to clinical trial execution. Perhaps most challenging and important of all are the difficulties with clinical trial accrual that can prevent study completion. Both the innovations and the challenges highlight the important role of process in progress in clinical oncology.