Rita Nanda
· Associate Professor of MedicineUniversity of Chicago · Hematology and Blood and Marrow Transplantation
Active 2001–2026
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About
Rita Nanda, MD, is an Associate Professor of Medicine in the Department of Medicine at The University of Chicago. Her clinical and research interests focus on breast cancer, including the use of advanced imaging techniques such as ultrafast DCE-MRI to predict response to neoadjuvant chemotherapy, and the application of gene expression signatures of systemic immunity to understand tumor microenvironment biology and therapeutic response. Her work also encompasses the evaluation of targeted therapies such as CDK4/6 inhibitors in hormone receptor-positive, HER2-negative breast cancer, as well as the study of treatment outcomes, toxicity, and mortality in breast cancer survivors, including those with a history of childhood cancer. Dr. Nanda's research contributes to improving personalized treatment strategies and understanding disparities in breast cancer care.
Research topics
- Medicine
- Oncology
- Internal medicine
- Biology
- Computer Science
- Artificial Intelligence
- Machine Learning
- Surgery
- Cancer research
- Nuclear medicine
Selected publications
JAMA Oncology · 2020 · 695 citations
1st authorCorrespondingImportance: Approximately 25% of patients with early-stage breast cancer who receive (neo)adjuvant chemotherapy experience a recurrence within 5 years. Improvements in therapy are greatly needed. Objective: To determine if pembrolizumab plus neoadjuvant chemotherapy (NACT) in early-stage breast cancer is likely to be successful in a 300-patient, confirmatory randomized phase 3 neoadjuvant clinical trial. Design, Setting, and Participants: The I-SPY2 study is an ongoing open-label, multicenter, a…
Journal of Clinical Oncology · 2020 · 490 citations
PURPOSE: 2. METHODS: (cohort 2). Prior PARPi, platinum-refractory disease, or progression on more than two chemotherapy regimens (metastatic setting) was not allowed. Patients received olaparib 300 mg orally twice a day until progression. A single-arm, two-stage design was used. The primary endpoint was objective response rate (ORR); the null hypothesis (≤ 5% ORR) would be rejected within each cohort if there were four or more responses in 27 patients. Secondary endpoints included clinical benef…
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Nature Communications · 2021 · 269 citations
The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites ca…
JAMA Oncology · 2020 · 220 citations
Importance: Pathologic complete response (pCR) is a known prognostic biomarker for long-term outcomes. The I-SPY2 trial evaluated if the strength of this clinical association persists in the context of a phase 2 neoadjuvant platform trial. Objective: To evaluate the association of pCR with event-free survival (EFS) and pCR with distant recurrence-free survival (DRFS) in subpopulations of women with high-risk operable breast cancer treated with standard therapy or one of several novel agents. Des…
JAMA Oncology · 2023 · 131 citations
Importance: Given conflicting results regarding the prognosis of erb-b2 receptor tyrosine kinase 2 (ERBB2; formerly HER2 or HER2/neu)-low breast cancer, a large-scale, nationally applicable comparison of ERBB2-low vs ERBB2-negative breast cancer is needed. Objective: To investigate whether ERBB2-low breast cancer is a clinically distinct subtype in terms of epidemiological characteristics, prognosis, and response to neoadjuvant chemotherapy. Design/Participants/Setting: This retrospective cohort…
Recent grants
Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant Therapy
NIH · $4.5M · 2010–2022
NIH · $2.1M · 2018–2021
Frequent coauthors
- 999 shared
HS Rugo
UCSF Helen Diller Family Comprehensive Cancer Center
- 976 shared
Christina Yau
University of California, San Francisco
- 938 shared
Angela DeMichele
University of Pennsylvania
- 921 shared
Lajos Pusztai
- 900 shared
Claudine Isaacs
- 887 shared
Douglas Yee
Masonic Cancer Center
- 882 shared
MC Liu
- 823 shared
Ruby Singhrao
University of California, San Francisco
Labs
Education
- 1998
Medical Degree
University of Chicago
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