Jennifer Caswell-Jin, MD is an instructor in medical oncology specializing in breast cancer care and research. She received her medical degree from Harvard Medical School in 2010, completed her internal medicine residency at the University of California, San Francisco in 2013, and completed her clinical fellowship in medical oncology at Stanford Hospital & Clinics in 2017.
Honors & Awards
AACR Associate Member Award, San Antonio Breast Cancer Symposium (2018)
Physician Scientist Training Award, Damon Runyon Cancer Research Foundation (2017)
Postdoctoral Fellowship Award, Susan G. Komen (2017)
ASCO Young Investigator Award, Conquer Cancer Foundation (2017)
SCI Fellowship Award, Stanford Cancer Institute (2017)
Fellowship: Stanford University Hematology and Oncology Fellowship (2017) CA
Board Certification: American Board of Internal Medicine, Oncology (2018)
Board Certification: American Board of Internal Medicine, Internal Medicine (2013)
Residency: UCSF Internal Medicine Residency (2013) CA
Medical Education: Harvard Medical School (2010) MA
A.B., Harvard College, Biological Anthropology (2006)
Current Research and Scholarly Interests
My research is on the translational application of next-generation sequencing technologies to breast cancer care: (1) the value of hereditary cancer genetic panel testing in clinical practice, (2) the mechanisms by which inherited genetic variants lead to breast cancer development, and (3) the analysis of somatic tumor sequencing data to inform understanding of breast tumorigenesis, metastasis, and development of resistance in response to therapeutics.
Graduate and Fellowship Programs
Oncology (Fellowship Program)
- Characterizing the tumor and immune microenvironment through treatment to predict response to neoadjuvant HER2-targeted therapy using the Digital Spatial Profiler AMER ASSOC CANCER RESEARCH. 2020
- Tumor expression and microenvironment in HER2-positive breast cancer before and on HER2-targeted therapy: Analysis of microarray expression data from the TRIO-US B07 trial AMER ASSOC CANCER RESEARCH. 2020
- Prevalence of Pathogenic Variants in Cancer Susceptibility Genes Among Women With Postmenopausal Breast Cancer. JAMA 2020; 323 (10): 995–97
- Re: Cascade Genetic Testing of Relatives for Hereditary Cancer Risk: Results of an Online Initiative Response JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE 2019; 111 (8): 874
Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.
The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related deathand death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprisingabout one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.
View details for PubMedID 30867590
Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data.
2019; 2 (4): 528–37
Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data.We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results Program (SEER). We identified de novo MBC patients from CCR and extracted information on distant recurrences from patient notes in EMR. Furthermore, we trained a regularized logistic regression model for recurrent MBC classification and evaluated its performance on a gold standard set of 146 patients.There were 11 459 breast cancer patients in total and the median follow-up time was 96.3 months. We identified 1886 MBC patients, 512 (27.1%) of whom were de novo MBC patients and 1374 (72.9%) were recurrent MBC patients. Our final MBC classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.917, with sensitivity 0.861, specificity 0.878, and accuracy 0.870.To enable population-based research on MBC, we developed a framework for retrospective case detection combining EMR and CCR data. Our classifier achieved good AUC, sensitivity, and specificity without expert-labeled examples.
View details for DOI 10.1093/jamiaopen/ooz040
View details for PubMedID 32025650
View details for PubMedCentralID PMC6994019
- Cascade Genetic Testing of Relatives for Hereditary Cancer Risk: Results of an Online Initiative JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE 2019; 111 (1): 95–98
Response to Peshkin, Isaacs, and Schwartz.
Journal of the National Cancer Institute
View details for PubMedID 30838406
Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data.
View details for DOI 10.1093/jamiaopen/ooz040
Chromatin regulators mediate anthracycline sensitivity in breast cancer.
Anthracyclines are a highly effective component of curative breast cancer chemotherapy but are associated with substantial morbidity1,2. Because anthracyclines work in part by inhibiting topoisomerase-II (TOP2) on accessible DNA3,4, we hypothesized that chromatin regulatory genes (CRGs) that mediate DNA accessibility might predict anthracycline response. We studied the role of CRGs in anthracycline sensitivity in breast cancer through integrative analysis of patient and cell line data. We identified a consensus set of 38 CRGs associated with anthracycline response across ten cell line datasets. By evaluating the interaction between expression and treatment in predicting survival in a metacohort of 1006 patients with early-stage breast cancer, we identified 54 CRGs whose expression levels dictate anthracycline benefit across the clinical subgroups; of these CRGs, 12 overlapped with those identified in vitro. CRGs that promote DNA accessibility, including Trithorax complex members, were associated with anthracycline sensitivity when highly expressed, whereas CRGs that reduce accessibility, such as Polycomb complex proteins, were associated with decreased anthracycline sensitivity. We show that KDM4B modulates TOP2 accessibility to chromatin, elucidating a mechanism of TOP2 inhibitor sensitivity. These findings indicate that CRGs mediate anthracycline benefit by altering DNA accessibility, with implications for the stratification of patients with breast cancer and treatment decision making.
View details for DOI 10.1038/s41591-019-0638-5
View details for PubMedID 31700186
Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy.
2019; 10 (1): 657
Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy. Two tumors underwent clonal replacement with treatment, and mathematical modeling indicates these two tumors had resistant subclones prior to treatment and rates of resistance-related genomic changes that were substantially larger than previous estimates. Our results provide a needed framework to incorporate primary tumor heterogeneity in investigating the evolution of resistance.
View details for PubMedID 30737380
Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer.
JCO clinical cancer informatics
2019; 3: 1–12
Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand cancer survival outcomes. We developed a natural language processing (NLP) system to identify patient-specific timelines of metastatic breast cancer recurrence.We used the OncoSHARE database, which includes merged data from the California Cancer Registry and EMRs of 8,956 women diagnosed with breast cancer in 2000 to 2018. We curated a comprehensive vocabulary by interviewing expert clinicians and processing radiology and pathology reports and progress notes. We developed and evaluated the following two distinct NLP approaches to analyze free-text notes: a traditional rule-based model, using rules for metastatic detection from the literature and curated by domain experts; and a contemporary neural network model. For each 3-month period (quarter) from 2000 to 2018, we applied both models to infer recurrence status for that quarter. We trained the NLP models using 894 randomly selected patient records that were manually reviewed by clinical experts and evaluated model performance using 179 hold-out patients (20%) as a test set.The median follow-up time was 19 quarters (5 years) for the training set and 15 quarters (4 years) for the test set. The neural network model predicted the timing of distant metastatic recurrence with a sensitivity of 0.83 and specificity of 0.73, outperforming the rule-based model, which had a specificity of 0.35 and sensitivity of 0.88 (P < .001).We developed an NLP method that enables identification of the occurrence and timing of metastatic breast cancer recurrence from EMRs. This approach may be adaptable to other cancer sites and could help to unlock the potential of EMRs for research on real-world cancer outcomes.
View details for DOI 10.1200/CCI.19.00034
View details for PubMedID 31584836
Change in Survival in Metastatic Breast Cancer with Treatment Advances: Meta-Analysis and Systematic Review.
JNCI cancer spectrum
2018; 2 (4): pky062
Metastatic breast cancer (MBC) treatment has changed substantially over time, but we do not know whether survival post-metastasis has improved at the population level.We searched for studies of MBC patients that reported survival after metastasis in at least two time periods between 1970 and the present. We used meta-regression models to test for survival improvement over time in four disease groups: recurrent, recurrent estrogen (ER)-positive, recurrent ER-negative, and de novo stage IV. We performed sensitivity analyses based on bias in some studies that could lead earlier cohorts to include more aggressive cancers.There were 15 studies of recurrent MBC (N = 18 678 patients; 3073 ER-positive and 1239 ER-negative); meta-regression showed no survival improvement among patients recurring between 1980 and 1990, but median survival increased from 21 (95% confidence interval [CI] = 18 to 25) months to 38 (95% CI = 31 to 47) months from 1990 to 2010. For ER-positive MBC patients, median survival increased during 1990-2010 from 32 (95% CI = 23 to 43) to 57 (95% CI = 37 to 87) months, and for ER-negative MBC patients from 14 (95% CI = 11 to 19) to 33 (95% CI = 21 to 51) months. Among eight studies (N = 35 831) of de novo stage IV MBC, median survival increased during 1990-2010 from 20 (95% CI = 16 to 24) to 31 (95% CI = 24 to 39) months. Results did not change in sensitivity analyses.By bridging studies over time, we demonstrated improvements in survival for recurrent and de novo stage IV MBC overall and across ER-defined subtypes since 1990. These results can inform patient-doctor discussions about MBC prognosis and therapy.
View details for PubMedID 30627694
Pathogenic variants in less familiar cancer susceptibility genes: what happens after genetic testing?
JCO Precision Oncology
View details for DOI 10.1200/PO.18.00167
Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk.
Genetics in medicine : official journal of the American College of Medical Genetics
PurposeWe examined racial/ethnic differences in the usage and results of germ-line multiple-gene sequencing (MGS) panels to evaluate hereditary cancer risk.MethodsWe collected genetic testing results and clinical information from 1,483 patients who underwent MGS at Stanford University between 1 January 2013 and 31 December 2015.ResultsAsians and Hispanics presented for MGS at younger ages than whites (48 and 47 vs. 55; P = 5E-16 and 5E-14). Across all panels, the rate of pathogenic variants (15%) did not differ significantly between racial groups. Rates by gene did differ: in particular, a higher percentage of whites than nonwhites carried pathogenic CHEK2 variants (3.8% vs. 1.0%; P = 0.002). The rate of a variant of uncertain significance (VUS) result was higher in nonwhites than whites (36% vs. 27%; P = 2E-4). The probability of a VUS increased with increasing number of genes tested; this effect was more pronounced for nonwhites than for whites (1.1% absolute difference in VUS rates testing BRCA1/2 vs. 8% testing 13 genes vs. 14% testing 28 genes), worsening the disparity.ConclusionIn this diverse cohort undergoing MGS testing, pathogenic variant rates were similar between racial/ethnic groups. By contrast, VUS results were more frequent among nonwhites, with potential significance for the impact of MGS testing by race/ethnicity.GENETICS in MEDICINE advance online publication, 27 July 2017; doi:10.1038/gim.2017.96.
View details for PubMedID 28749474
- Acute, Unilateral Breast Toxicity From Gemcitabine in the Setting of Thoracic Inlet Obstruction. Journal of oncology practice / American Society of Clinical Oncology 2016; 12 (8): 763-764
Multiple breast cancer risk variants are associated with differential transcript isoform expression in tumors
HUMAN MOLECULAR GENETICS
2015; 24 (25): 7421-7431
Genome-wide association studies have identified over 70 single-nucleotide polymorphisms (SNPs) associated with breast cancer. A subset of these SNPs are associated with quantitative expression of nearby genes, but the functional effects of the majority remain unknown. We hypothesized that some risk SNPs may regulate alternative splicing. Using RNA-sequencing data from breast tumors and germline genotypes from The Cancer Genome Atlas, we tested the association between each risk SNP genotype and exon-, exon-exon junction- or transcript-specific expression of nearby genes. Six SNPs were associated with differential transcript expression of seven nearby genes at FDR < 0.05 (BABAM1, DCLRE1B/PHTF1, PEX14, RAD51L1, SRGAP2D and STXBP4). We next developed a Bayesian approach to evaluate, for each SNP, the overlap between the signal of association with breast cancer and the signal of association with alternative splicing. At one locus (SRGAP2D), this method eliminated the possibility that the breast cancer risk and the alternate splicing event were due to the same causal SNP. Lastly, at two loci, we identified the likely causal SNP for the alternative splicing event, and at one, functionally validated the effect of that SNP on alternative splicing using a minigene reporter assay. Our results suggest that the regulation of differential transcript isoform expression is the functional mechanism of some breast cancer risk SNPs and that we can use these associations to identify causal SNPs, target genes and the specific transcripts that may mediate breast cancer risk.
View details for DOI 10.1093/hmg/ddv432
View details for Web of Science ID 000368373600021
View details for PubMedID 26472073
View details for PubMedCentralID PMC4664170
Genome-wide association study identifies variants at 16p13 associated with survival in multiple myeloma patients
Here we perform the first genome-wide association study (GWAS) of multiple myeloma (MM) survival. In a meta-analysis of 306 MM patients treated at UCSF and 239 patients treated at the Mayo clinic, we find a significant association between SNPs near the gene FOPNL on chromosome 16p13 and survival (rs72773978; P=6 × 10(-10)). Patients with the minor allele are at increased risk for mortality (HR: 2.65; 95% CI: 1.94-3.58) relative to patients homozygous for the major allele. We replicate the association in the IMMEnSE cohort including 772 patients, and a University of Utah cohort including 318 patients (rs72773978 P=0.044). Using publicly available data, we find that the minor allele was associated with increased expression of FOPNL and increased expression of FOPNL was associated with higher expression of centrosomal genes and with shorter survival. Polymorphisms at the FOPNL locus are associated with survival among MM patients.
View details for DOI 10.1038/ncomms8539
View details for Web of Science ID 000358852600004
View details for PubMedID 26198393
Genome-wide association study of breast cancer in Latinas identifies novel protective variants on 6q25
The genetic contributions to breast cancer development among Latinas are not well understood. Here we carry out a genome-wide association study of breast cancer in Latinas and identify a genome-wide significant risk variant, located 5' of the Estrogen Receptor 1 gene (ESR1; 6q25 region). The minor allele for this variant is strongly protective (rs140068132: odds ratio (OR) 0.60, 95% confidence interval (CI) 0.53-0.67, P=9 × 10(-18)), originates from Indigenous Americans and is uncorrelated with previously reported risk variants at 6q25. The association is stronger for oestrogen receptor-negative disease (OR 0.34, 95% CI 0.21-0.54) than oestrogen receptor-positive disease (OR 0.63, 95% CI 0.49-0.80; P heterogeneity=0.01) and is also associated with mammographic breast density, a strong risk factor for breast cancer (P=0.001). rs140068132 is located within several transcription factor-binding sites and electrophoretic mobility shift assays with MCF-7 nuclear protein demonstrate differential binding of the G/A alleles at this locus. These results highlight the importance of conducting research in diverse populations.
View details for DOI 10.1038/ncomms6260
View details for Web of Science ID 000343985400009
View details for PubMedCentralID PMC4204111
High mammographic density in women of Ashkenazi Jewish descent
BREAST CANCER RESEARCH
2013; 15 (3)
Percent mammographic density (PMD) adjusted for age and body mass index is one of the strongest risk factors for breast cancer and is known to be approximately 60% heritable. Here we report a finding of an association between genetic ancestry and adjusted PMD.We selected self-identified Caucasian women in the California Pacific Medical Center Research Institute Cohort whose screening mammograms placed them in the top or bottom quintiles of age-adjusted and body mass index-adjusted PMD. Our final dataset included 474 women with the highest adjusted PMD and 469 with the lowest genotyped on the Illumina 1 M platform. Principal component analysis (PCA) and identity-by-descent analyses allowed us to infer the women's genetic ancestry and correlate it with adjusted PMD.Women of Ashkenazi Jewish ancestry, as defined by the first principal component of PCA and identity-by-descent analyses, represented approximately 15% of the sample. Ashkenazi Jewish ancestry, defined by the first principal component of PCA, was associated with higher adjusted PMD (P = 0.004). Using multivariate regression to adjust for epidemiologic factors associated with PMD, including age at parity and use of postmenopausal hormone therapy, did not attenuate the association.Women of Ashkenazi Jewish ancestry, based on genetic analysis, are more likely to have high age-adjusted and body mass index-adjusted PMD. Ashkenazi Jews may have a unique set of genetic variants or environmental risk factors that increase mammographic density.
View details for DOI 10.1186/bcr3424
View details for Web of Science ID 000328937600004
View details for PubMedID 23668689
8q24 prostate, breast, and colon cancer risk loci show tissue-specific long-range interaction with MYC
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2010; 107 (21): 9742-9746
The 8q24 gene desert contains risk loci for multiple epithelial cancers, including colon, breast, and prostate. Recent evidence suggests these risk loci contain enhancers. In this study, data are presented showing that each risk locus bears epigenetic marks consistent with enhancer elements and forms a long-range chromatin loop with the MYC proto-oncogene located several hundred kilobases telomeric and that these interactions are tissue-specific. We therefore propose that the 8q24 risk loci operate through a common mechanism-as tissue-specific enhancers of MYC.
View details for DOI 10.1073/pnas.0910668107
View details for Web of Science ID 000278054700049
View details for PubMedID 20453196
Analysis of chimpanzee history based on genome sequence alignments
2008; 4 (4)
Population geneticists often study small numbers of carefully chosen loci, but it has become possible to obtain orders of magnitude for more data from overlaps of genome sequences. Here, we generate tens of millions of base pairs of multiple sequence alignments from combinations of three western chimpanzees, three central chimpanzees, an eastern chimpanzee, a bonobo, a human, an orangutan, and a macaque. Analysis provides a more precise understanding of demographic history than was previously available. We show that bonobos and common chimpanzees were separated approximately 1,290,000 years ago, western and other common chimpanzees approximately 510,000 years ago, and eastern and central chimpanzees at least 50,000 years ago. We infer that the central chimpanzee population size increased by at least a factor of 4 since its separation from western chimpanzees, while the western chimpanzee effective population size decreased. Surprisingly, in about one percent of the genome, the genetic relationships between humans, chimpanzees, and bonobos appear to be different from the species relationships. We used PCR-based resequencing to confirm 11 regions where chimpanzees and bonobos are not most closely related. Study of such loci should provide information about the period of time 5-7 million years ago when the ancestors of humans separated from those of the chimpanzees.
View details for DOI 10.1371/journal.pgen.1000057
View details for Web of Science ID 000255407400011
View details for PubMedID 18421364