Amrita Basu, PhD

Assistant Adjunct Professor
Surgery
[email protected]

Development of computational models for early detection of cancer lesions and progression to metastatic disease can help discriminate between high and low cancer risk profiles. Therefore, we seek to exploit diverse, high-throughput genomic and clinical data to understand the molecular networks underlying fundamental cellular processes that can eventually stratify patients by non-traditional underpinnings, including transcriptional regulation, epigenetic signaling, and chemosensitivity. Our algorithmic methods draw on machine learning, a computational field concerned with learning accurate, predictive models from noisy and high-dimensional data.

Another area of research includes developing standardized methods and measures to integrate drug toxicity, quality of life, and efficacy measures for breast cancer patients. We are building infrastructure and tools to support patient-reported outcomes collection and downstream analysis and visualization.

Publications: 

Implementation and impact of an electronic patient reported outcomes system in a phase II multi-site adaptive platform clinical trial for early-stage breast cancer.

Journal of the American Medical Informatics Association : JAMIA

Northrop A, Christofferson A, Umashankar S, Melisko M, Castillo P, Brown T, Heditsian D, Brain S, Simmons C, Hieken T, Ruddy KJ, Mainor C, Afghahi A, Tevis S, Blaes A, Kang I, Asare A, Esserman L, Hershman DL, Basu A

Magnetic resonance imaging insights from active surveillance of women with ductal carcinoma in situ.

NPJ breast cancer

Greenwood HI, Maldonado Rodas CK, Freimanis RI, Glencer AC, Miller PN, Mukhtar RA, Brabham C, Yau C, Rosenbluth JM, Hirst GL, Campbell MJ, Borowsky A, Hylton N, Esserman LJ, Basu A

The "PRO"mise and "PRO"gress of PROs in cancer clinical trials.

Journal of the National Cancer Institute

Basu A, Hershman DL

B-cells and regulatory T-cells in the microenvironment of HER2+ breast cancer are associated with decreased survival: a real-world analysis of women with HER2+ metastatic breast cancer.

Breast cancer research : BCR

Steenbruggen TG, Wolf DM, Campbell MJ, Sanders J, Cornelissen S, Thijssen B, Salgado RA, Yau C, O-Grady N, Basu A, Bhaskaran R, Mittempergher L, Hirst GL, Coppe JP, Kok M, Sonke GS, van 't Veer LJ, Horlings HM

Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatments and receptor subtypes.

Frontiers in oncology

Yu K, Basu A, Yau C, Wolf DM, Goodarzi H, Bandyopadhyay S, Korkola JE, Hirst GL, Asare S, DeMichele A, Hylton N, Yee D, Esserman L, van 't Veer L, Sirota M

Outcomes and clinicopathologic characteristics associated with disseminated tumor cells in bone marrow after neoadjuvant chemotherapy in high-risk early stage breast cancer: the I-SPY SURMOUNT study.

Breast cancer research and treatment

Magbanua MJM, van 't Veer L, Clark AS, Chien AJ, Boughey JC, Han HS, Wallace A, Beckwith H, Liu MC, Yau C, Wileyto EP, Ordonez A, Solanki TI, Hsiao F, Lee JC, Basu A, Brown Swigart L, Perlmutter J, Delson AL, Bayne L, Deluca S, Yee SS, Carpenter EL, Esserman LJ, Park JW, Chodosh LA, DeMichele A

Identifying Good Candidates for Active Surveillance of Ductal Carcinoma In Situ: Insights from a Large Neoadjuvant Endocrine Therapy Cohort.

Cancer Research Communications

Glencer AC, Miller PN, Greenwood H, Maldonado Rodas CK, Freimanis R, Basu A, Mukhtar RA, Brabham C, Kim P, Hwang ES, Rosenbluth JM, Hirst GL, Campbell MJ, Borowsky AD, Esserman LJ

Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies.

Cancer cell

Wolf DM, Yau C, Wulfkuhle J, Brown-Swigart L, Gallagher IR, Lee PRE, Zhu Z, Magbanua MJ, Sayaman R, O'Grady N, Basu A, Delson A, Coppé JP, Lu R, Braun J, I-SPY2 Investigators, Asare SM, Sit L, Matthews JB, Perlmutter J, Hylton N, Liu MC, Pohlmann P, Symmans WF, Rugo HS, Isaacs C, DeMichele AM, Yee D, Berry DA, Pusztai L, Petricoin EF, Hirst GL, Esserman LJ, van 't Veer LJ

Treatment Efficacy Score - continuous residual cancer burden-based metric to compare neoadjuvant chemotherapy efficacy between randomized trial arms in breast cancer trials.

Annals of oncology : official journal of the European Society for Medical Oncology

Marczyk M, Mrukwa A, Yau C, Wolf D, Chen YY, Balassanian R, Nanda R, Parker BA, Krings G, Sattar H, Zeck JC, Albain KS, Boughey JC, Liu MC, Elias AD, Clark AS, Venters SJ, Shad S, Basu A, Asare SM, Buxton M, Asare AL, Rugo HS, Perlmutter J, DeMichele AM, Yee D, Berry DA, van 't Veer L, Symmans WF, Esserman L, Pusztai L, I-SPY Consortium

ATRX promotes heterochromatin formation to protect cells from G-quadruplex DNA-mediated stress.

Nature communications

Teng YC, Sundaresan A, O'Hara R, Gant VU, Li M, Martire S, Warshaw JN, Basu A, Banaszynski LA

PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trial.

JAMIA open

O'Grady N, Gibbs DL, Abdilleh K, Asare A, Asare S, Venters S, Brown-Swigart L, Hirst GL, Wolf D, Yau C, van 't Veer LJ, Esserman L, Basu A

The quality of life index: a pilot study integrating treatment efficacy and quality of life in oncology.

NPJ breast cancer

Basu A, Philip EJ, Dewitt B, Hanmer J, Chattopadhyay A, Yau C, Melisko ME, Esserman LJ

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

JCO clinical cancer informatics

Barnholtz-Sloan JS, Rollison DE, Basu A, Borowsky AD, Bui A, DiGiovanna J, Garcia-Closas M, Genkinger JM, Gerke T, Induni M, Lacey JV, Mirel L, Permuth JB, Saltz J, Shenkman EA, Ulrich CM, Zheng WJ, Nadaf S, Kibbe WA

Call for Data Standardization: Lessons Learned and Recommendations in an Imaging Study.

JCO clinical cancer informatics

Basu A, Warzel D, Eftekhari A, Kirby JS, Freymann J, Knable J, Sharma A, Jacobs P

RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines.

Bioinformatics (Oxford, England)

Basu A, Mitra R, Liu H, Schreiber SL, Clemons PA

An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.

Cell

Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, Ebright RY, Stewart ML, Ito D, Wang S, Bracha AL, Liefeld T, Wawer M, Gilbert JC, Wilson AJ, Stransky N, Kryukov GV, Dancik V, Barretina J, Garraway LA, Hon CS, Munoz B, Bittker JA, Stockwell BR, Khabele D, Stern AM, Clemons PA, Shamji AF, Schreiber SL

mChIP-KAT-MS, a method to map protein interactions and acetylation sites for lysine acetyltransferases.

Proceedings of the National Academy of Sciences of the United States of America

Mitchell L, Huard S, Cotrut M, Pourhanifeh-Lemeri R, Steunou AL, Hamza A, Lambert JP, Zhou H, Ning Z, Basu A, Côté J, Figeys DA, Baetz K

Computational prediction of lysine acetylation proteome-wide.

Methods in molecular biology (Clifton, N.J.)

Basu A

Proteome-wide prediction of acetylation substrates.

Proceedings of the National Academy of Sciences of the United States of America

Basu A, Rose KL, Zhang J, Beavis RC, Ueberheide B, Garcia BA, Chait B, Zhao Y, Hunt DF, Segal E, Allis CD, Hake SB

Polycomb Group proteins: an evolutionary perspective.

Trends in genetics : TIG

Whitcomb SJ, Basu A, Allis CD, Bernstein E