Clinical Instructor, Medicine - Hematology
Board Certification, American Board of Internal Medicine, Hematology (2013)
Board Certification, American Board of Internal Medicine, Internal Medicine (2011)
Fellowship, Stanford University, Hematology/Oncology (2013)
Residency, Stanford University, Internal Medicine (2009)
Internship, Stanford University, Internal Medicine (2008)
Doctor of Medicine, Stanford University, MED-MD (2007)
Bachelor of Science, Massachusetts Institute of Technology, Chemical Engineering, Biology (2001)
Preleukemic mutations in human acute myeloid leukemia affect epigenetic regulators and persist in remission.
Proceedings of the National Academy of Sciences of the United States of America
2014; 111 (7): 2548-53
Cancer is widely characterized by the sequential acquisition of genetic lesions in a single lineage of cells. Our previous studies have shown that, in acute myeloid leukemia (AML), mutation acquisition occurs in functionally normal hematopoietic stem cells (HSCs). These preleukemic HSCs harbor some, but not all, of the mutations found in the leukemic cells. We report here the identification of patterns of mutation acquisition in human AML. Our findings support a model in which mutations in "landscaping" genes, involved in global chromatin changes such as DNA methylation, histone modification, and chromatin looping, occur early in the evolution of AML, whereas mutations in "proliferative" genes occur late. Additionally, we analyze the persistence of preleukemic mutations in patients in remission and find CD34+ progenitor cells and various mature cells that harbor preleukemic mutations. These findings indicate that preleukemic HSCs can survive induction chemotherapy, identifying these cells as a reservoir for the reevolution of relapsed disease. Finally, through the study of several cases of relapsed AML, we demonstrate various evolutionary patterns for the generation of relapsed disease and show that some of these patterns are consistent with involvement of preleukemic HSCs. These findings provide key insights into the monitoring of minimal residual disease and the identification of therapeutic targets in human AML.
View details for DOI 10.1073/pnas.1324297111
View details for PubMedID 24550281
- How we treat: risk mitigation for ABO-incompatible plasma in plateletpheresis products TRANSFUSION 2012; 52 (10): 2081-2085
Unfavorable-risk cytogenetics in acute myeloid leukemia
EXPERT REVIEW OF HEMATOLOGY
2011; 4 (2): 173-184
Cytogenetic analysis at diagnosis is one of the most significant prognostic factors in acute myeloid leukemia (AML). AML patients with unfavorable-risk cytogenetic abnormalities account for 16-30% of younger adult patients and have poor response to standard treatment, with only 32-55% achieving a complete response. Overall survival is also extremely poor with only 5-12% patients alive at 5-10 years after diagnosis. Owing to the poor response in this subset of patients, risk-adapted treatment has been investigated. Allogeneic stem cell transplant has been shown to provide a survival benefit in patients with unfavorable-risk cytogenetic abnormalities in complement receptor 1. Other risk-adapted treatment strategies, such as reduced-intensity conditioning regimens prior to allogeneic stem cell transplant for older patients with AML, have also shown some survival benefit, without increasing treatment-related toxicities. Risk-stratification models that include cytogenetic abnormalities, as well as other molecular markers, are being developed to allow for individualized risk-adapted treatment for patients with AML. Prospective multicenter trials will be needed to validate these prognostic models.
View details for DOI 10.1586/EHM.11.10
View details for Web of Science ID 000290371000014
View details for PubMedID 21495927
Local false discovery rate facilitates comparison of different microarray experiments
NUCLEIC ACIDS RESEARCH
2009; 37 (22): 7483-7497
The local false discovery rate (LFDR) estimates the probability of falsely identifying specific genes with changes in expression. In computer simulations, LFDR <10% successfully identified genes with changes in expression, while LFDR >90% identified genes without changes. We used LFDR to compare different microarray experiments quantitatively: (i) Venn diagrams of genes with and without changes in expression, (ii) scatter plots of the genes, (iii) correlation coefficients in the scatter plots and (iv) distributions of gene function. To illustrate, we compared three methods for pre-processing microarray data. Correlations between methods were high (r = 0.84-0.92). However, responses were often different in magnitude, and sometimes discordant, even though the methods used the same raw data. LFDR complements functional assessments like gene set enrichment analysis. To illustrate, we compared responses to ultraviolet radiation (UV), ionizing radiation (IR) and tobacco smoke. Compared to unresponsive genes, genes responsive to both UV and IR were enriched for cell cycle, mitosis, and DNA repair functions. Genes responsive to UV but not IR were depleted for cell adhesion functions. Genes responsive to tobacco smoke were enriched for detoxification functions. Thus, LFDR reveals differences and similarities among experiments.
View details for DOI 10.1093/nar/gkp813
View details for Web of Science ID 000272935000021
View details for PubMedID 19825981
- Immune signatures in follicular lymphoma NEW ENGLAND JOURNAL OF MEDICINE 2005; 352 (14): 1496-1496
Toxicity from radiation therapy associated with abnormal transcriptional responses to DNA damage
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2004; 101 (17): 6635-6640
Toxicity from radiation therapy is a grave problem for cancer patients. We hypothesized that some cases of toxicity are associated with abnormal transcriptional responses to radiation. We used microarrays to measure responses to ionizing and UV radiation in lymphoblastoid cells derived from 14 patients with acute radiation toxicity. The analysis used heterogeneity-associated transformation of the data to account for a clinical outcome arising from more than one underlying cause. To compute the risk of toxicity for each patient, we applied nearest shrunken centroids, a method that identifies and cross-validates predictive genes. Transcriptional responses in 24 genes predicted radiation toxicity in 9 of 14 patients with no false positives among 43 controls (P = 2.2 x 10(-7)). The responses of these nine patients displayed significant heterogeneity. Of the five patients with toxicity and normal responses, two were treated with protocols that proved to be highly toxic. These results may enable physicians to predict toxicity and tailor treatment for individual patients.
View details for DOI 10.1073/pnas.0307761101
View details for Web of Science ID 000221107900056
View details for PubMedID 15096622