Amrita Basu

PURPOSE: Neoadjuvant immunotherapy (IO) has become the standard of care for early-stage triple-negative breast cancer (TNBC), but not yet for other subtypes. We previously developed a clinical-grade mRNA-based immune classifier (ImPrint) predicting response to IO that is now being used in I-SPY2.2 as part of the response predictive subtypes. We report the performance of ImPrint in hormone receptor–positive and human epidermal growth factor receptor 2–negative (HR+HER2–) patients from five IO arms. METHODS: A total of 204 HR+HER2– (MammaPrint high-risk) patients from five IO arms (anti–PD-1, anti–PD-L1/poly [ADP-ribose] polymerase inhibitor combination, anti–PD-1/toll-like receptor 9 dual-IO combination, and anti–PD-1 ± lymphocyte activation gene 3 dual-IO combination) and 191 patients from the chemotherapy-only control arm were included in this analysis. Patients were classified as ImPrint+ (likely sensitive) versus ImPrint– (likely resistant), using pretreatment mRNA. Performance of ImPrint for predicting pathologic complete response (pCR) to IO-containing arms was characterized and compared with tumor grade (III), MammaPrint (ultra) High2 risk (MP2), and estrogen receptor (ER)-low (ER ≤ 10%). RESULTS: Overall, the pCR rate across the five IO arms was 33%. 26% of HR+HER2– patients were ImPrint+, and pCR rates with IO were 75% in ImPrint+ versus 17% in ImPrint–, with the highest pCR rate >90% in a dual-IO arm. In the control arm, pCR rates were 33% in ImPrint+ and 8% in ImPrint–. Tumor grade (III), MP2, and ER-low showed pCR rates in IO of 45%, 56%, and 63%, respectively, with lower pCR odds ratios (OR < 7.5) compared with ImPrint (OR = 14.5). CONCLUSION: Using an accurate selection strategy, HR+HER2– patients could achieve pCR rates similar to what is seen with best neoadjuvant therapies in TNBC and HER2+ (ie, pCR rate >65%-70%). ImPrint, an Food and Drug Administration IDE-enabled assay, may represent a way to identify HR+HER2– patients for IO that best balances likely benefit versus risk of serious immune-related adverse events.

We describe the development and implementation of a system for monitoring patient-reported adverse events and quality of life using electronic Patient Reported Outcome (ePRO) instruments in the I-SPY2 Trial, a phase II clinical trial for locally advanced breast cancer. We describe the administration of technological, workflow, and behavior change interventions and their associated impact on questionnaire completion. Using the OpenClinica electronic data capture system, we developed rules-based logic to build automated ePRO surveys, customized to the I-SPY2 treatment schedule. We piloted ePROs at the University of California, San Francisco (UCSF) to optimize workflow in the context of trial treatment scenarios and staggered rollout of the ePRO system to 26 sites to ensure effective implementation of the technology. Increasing ePRO completion requires workflow solutions and research staff engagement. Over two years, we increased baseline survey completion from 25% to 80%. The majority of patients completed between 30% and 75% of the questionnaires they received, with no statistically significant variation in survey completion by age, race or ethnicity. Patients who completed the screening timepoint questionnaire were significantly more likely to complete more of the surveys they received at later timepoints (mean completion of 74.1% vs 35.5%, P < .0001). Baseline PROMIS social functioning and grade 2 or more PRO-CTCAE interference of Abdominal Pain, Decreased Appetite, Dizziness and Shortness of Breath was associated with lower survey completion rates. By implementing ePROs, we have the potential to increase efficiency and accuracy of patient-reported clinical trial data collection, while improving quality of care, patient safety, and health outcomes. Our method is accessible across demographics and facilitates an ease of data collection and sharing across nationwide sites. We identify predictors of decreased completion that can optimize resource allocation by better targeting efforts such as in-person outreach, staff engagement, a robust technical workflow, and increased monitoring to improve overall completion rates. https://clinicaltrials.gov/study/NCT01042379.

New approaches are needed to determine which ductal carcinoma in situ (DCIS) is at high risk for progression to invasive ductal carcinoma (IDC). We retrospectively studied DCIS patients who declined surgery (2002–2019), and received endocrine therapy (ET) and breast MRI. Baseline MRI and changes at 3 months and 6 months were analyzed by recursive partitioning to stratify IDC risk. Sixty-two patients (63 DCIS; 1 bilateral) with a mean follow-up of 8.5 years were included. Fifty-one percent remained on active surveillance (AS) without evidence of IDC, with a mean duration of 7.6 years. A decision tree based on MRI features of lesion distinctness and background parenchymal enhancement (BPE) at baseline and change after 3 months of ET stratified patients into low, intermediate, and high risk for progression to IDC. MRI imaging features in patients treated with ET and undergoing AS, may help determine which DCIS lesions are at low versus high risk for IDC.

Background: Comorbidities and symptoms in metastatic breast cancer patients impact treatment decisions and influence prognosis and quality of life. The objective of this study is to examine the concordance between physician documentation and patient reports of comorbidities and symptoms to understand their comparative effectiveness.Methods: New patients with metastatic breast cancer completed an electronic intake survey assessing patient health history and symptoms. Physician documentation across 54 comorbidities and 42 symptoms was abstracted from notes for the corresponding clinic visits between November 2016 and March 2020. Concordance between patient reports and medical records for each condition and hazards ratios for each patient versus physician reported comorbidity and symptom were assessed.Results: A total of 168 patients were included in the analysis (age, median = 56 years, range = 29–86 years; 131 white [78.9%]). Twenty‐three of 54 comorbidities had a moderate to high level of agreement between patients and physicians (κ ≥ 0.40). Physicians documented higher numbers of comorbidities that can be objectively measured which also had higher concordance (e.g., diabetes [κ = 0.83] and hypertension [κ = 0.79]) while patients reported higher numbers of comorbidities that are more subjective which also had lower concordance (anxiety [κ = 0.30], GERD [κ = 0.36]). One physician‐documented and two patient‐reported comorbidities were significantly associated with survival (p < 0.05). Only 2 of 42 symptoms had a moderate to high level of agreement between patients and physicians. One physician‐documented and nine patient‐reported symptoms were significantly associated with decreased survival (p < 0.05).Conclusion: Agreement between patients' and physicians' reporting of comorbidities varies substantially, and patient reports can complement physician documentation. Physicians significantly underreported symptoms versus patients; thus, concordance was also low. Multiple patient‐reported symptoms were predictive of survival; thus, incorporating them can provide more informative estimates of predicted survival.

Background: Symptom burden and reduced quality of life (QOL) are considerable hurdles in oncology. The authors used the Patient‐Reported Outcomes Measurement Information System (PROMIS), which assesses physical and psychosocial health, to establish a mean symptom burden, examine potential drivers, and characterize severe symptom burden in breast cancer patient subgroups with the goal of characterizing stage IV patient QOL and triaging patients to individualized supportive care services.Methods: New patients at the University of California San Francisco Breast Care Center received questionnaires with 8 PROMIS domains: depression, anxiety, fatigue, sleep‐related impairment, sleep disturbance, cognitive function, cognitive abilities, and physical function. PROMIS values were scored with the HealthMeasures service and were compared by age, cancer stage, and educational status.Results: Stage IV patients with breast cancer (n = 169) reported higher depression and fatigue and worse cognitive function, cognitive abilities, and physical function than patients with stage 0 to III disease (n = 2577). As age increased, cognitive function impairment, depression, anxiety, and sleep‐related symptoms decreased. More educated patients showed better physical function and less severe sleep disturbance and fatigue. Across all subgroups, patients with high anxiety had the greatest probability of worse symptom burden and function in other domains.CONCLUSIONS: This study provides an additional set of PROMIS population estimates across breast cancer demographic groups. The analysis of a large stage IV population reinforces that metastatic patients have impaired QOL across multiple domains. Because anxiety emerged as a potential driver of impaired QOL in other domains, earlier interventions to reduce anxiety could improve QOL overall. These analyses will help to determine appropriate thresholds of intervention.Lay Summary: Patients receiving treatment for breast cancer can experience decreased quality of life. This study characterized differences in self‐reported quality of life among patients of different ages, with different stages of cancer, and with different educational backgrounds. This study also examined the effect of decreased quality of life in one area (eg, anxiety) on another area (eg, difficulty in sleeping). Patients who were younger, had not attended college or technical school, or had stage IV cancer tended to have worse quality of life. Patients who had high levels of anxiety also tended to have high levels of impairment in other areas.

Objectives: In this paper, we discuss leveraging cloud-based platforms to collect, visualize, analyze, and share data in the context of a clinical trial. Our cloud-based infrastructure, Patient Repository of Biomolecular Entities (PRoBE), has given us the opportunity for uniform data structure, more efficient analysis of valuable data, and increased collaboration between researchers.Materials and Methods: We utilize a multi-cloud platform to manage and analyze data generated from the clinical Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2 (I-SPY 2 TRIAL). A collaboration with the Institute for Systems Biology Cancer Gateway in the Cloud has additionally given us access to public genomic databases. Applications to I-SPY 2 data have been built using R Shiny, while leveraging Google's BigQuery tables and SQL commands for data mining.Results: We highlight the implementation of PRoBE in several unique case studies including prediction of biomarkers associated with clinical response, access to the Pan-Cancer Atlas, and integrating pathology images within the cloud. Our data integration pipelines, documentation, and all codebase will be placed in a Github repository.Discussion and conclusion: We are hoping to develop risk stratification diagnostics by integrating additional molecular, magnetic resonance imaging, and pathology markers into PRoBE to better predict drug response. A robust cloud infrastructure and tool set can help integrate these large datasets to make valuable predictions of response to multiple agents. For that reason, we are continuously improving PRoBE to advance the way data is stored, accessed, and analyzed in the I-SPY 2 clinical trial.

The majority of women diagnosed with breast cancer will experience some form of drug-related toxicity and subsequent impairments in Health-related Quality of Life (HRQoL). Despite this, HRQoL is assessed inconsistently and there is no validated method to integrate HRQoL data into the assessment of therapeutic agents. This proof of concept study utilizes data from the neoadjuvant I-SPY 2 clinical trial to describe the development of the Quality of Life Index (QoLI) measure. The QoLI represents a single composite score that incorporates validated longitudinal measures of clinical efficacy and QoL and one that permits a more comprehensive, direct comparison of individual therapeutic agents. Preliminary data suggest the QoLI is able to distinguish between agents based on their efficacy and toxicity; with further validation, the QoLI has the potential to provide more patient-centered evaluations in clinical trials and help guide treatment decision making in breast cancer and other oncologic diseases.

In recent years there have been several efforts to generate sensitivity profiles of collections of genomically characterized cell lines to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based on cellular, genetic, or expression biomarkers of cancer cells. However, a remaining challenge is an efficient way to identify accurate sets of biomarkers to validate. To address this challenge, we developed methodology using gene-expression profiles of human cancer cell lines to predict the responses of these cell lines to a panel of compounds. We developed an iterative weighting scheme which, when applied to elastic net, a regularized regression method, significantly improves the overall accuracy of predictions, particularly in the highly sensitive response region. In addition to application of these methods to actual chemical sensitivity data, we investigated the effects of sample size, number of features, model sparsity, signal-to-noise ratio, and feature correlation on predictive performance using a simulation framework, particularly for situations where the number of covariates is much larger than sample size. While our method aims to be useful in therapeutic discovery and understanding of the basic mechanisms of action of drugs and their targets, it is generally applicable in any domain where predictions of extreme responses are of highest importance. The iterative and other weighting algorithms were implemented in R. The code is available at https://github.com/kiwtir/RWEN. The CTRP data are available at ftp://caftpd.nci.nih.gov/pub/OCG-DCC/CTD2/Broad/CTRPv2.1_2016_pub_NatChemBiol_12_109/ and the Sanger data at ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/release-6.0/. Supplementary data are available at Bioinformatics online.