Susan M Domchek
· Basser Associate Professor in OncologyUniversity of Pennsylvania · Rehabilitation Medicine
Active 1992–2024
About
Susan M Domchek, MD, FASCO, FAACR, is the Basser Professor in Oncology at the University of Pennsylvania's Perelman School of Medicine. She serves as the Director of the Cancer Risk Evaluation Program at the Abramson Cancer Center and is a Senior Fellow at the Leonard Davis Institute of Health Economics. Additionally, she holds the position of Executive Director of the Basser Center for BRCA. Her clinical expertise encompasses breast cancer prevention, screening, and treatment, with a focus on breast cancer genetics. Her research expertise includes breast cancer genetics, prevention, screening, and immunotherapy clinical trials. Dr. Domchek's work involves understanding the genetic basis of breast cancer, developing targeted prevention strategies, and advancing immunotherapy approaches. She has contributed to numerous publications in the field, emphasizing her role as a leading figure in oncology and breast cancer research.
Research topics
- Oncology
- Medicine
- Internal medicine
- Biology
- Genetics
- Cancer research
- Political Science
- Gynecology
- Environmental health
- Computer Science
- Pathology
- Cardiology
- Data science
- Computational biology
- Obstetrics
- Bioinformatics
Selected publications
Journal of Clinical Oncology · 2024 · 21 citations
- Medicine
- Gynecology
- Internal medicine
PURPOSE: mutation carriers. METHODS: mutation carriers were assessed using Cox regression. RESULTS: = .7, respectively). CONCLUSION: mutations should carefully weigh the risks and benefits for each individual.
Cancer Research · 2023 · 3 citations
- Medicine
- Internal medicine
- Oncology
Abstract Purpose To estimate the risk of contralateral breast cancer (CBC) among women in the general population with germline pathogenic variants (PVs) in ATM, BRCA1, BRCA2, CHEK2, and PALB2. Methods Among 15,104 prospectively followed women within the CARRIERS study treated with ipsilateral surgery for invasive breast cancer, a subset of 14,237 women were identified from population-based studies. The risk of CBC was estimated for PV carriers in each gene compared to women without PVs in a multivariate proportional hazard regression analysis accounting for the competing risk of death and adjusting for patient and tumor characteristics. The primary analyses focused on the overall cohort and on women from the general population. Secondary analyses examined associations by race/ethnicity, age at primary breast cancer diagnosis, menopausal status, and tumor estrogen receptor status. Results Germline BRCA1, BRCA2, and CHEK2 PV carriers with breast cancer were at significantly elevated risk (Hazard ratio ≥ 1.9, p< 0.05) of CBC, whereas only the PALB2 PV carriers with ER-negative breast cancer had elevated risks. In contrast, ATM PV carriers did not have significantly increased CBC risks. African American PV carriers had similarly elevated risks of CBC as non-Hispanic White PV carriers. Among premenopausal women, the 15-year cumulative incidence of CBC was >20% for BRCA1, BRCA2 and CHEK2 PV carriers with breast cancer, and for PALB2 PV carriers with ER-negative breast cancer. The 15-year cumulative incidence of CBC among postmenopausal PV carriers was < 20% for PV carriers in any of the 5 genes. Conclusions Women diagnosed with breast cancer and known to carry germline PVs in BRCA1, BRCA2, CHEK2, or PALB2 are at substantially increased risk of CBC and may benefit from enhanced surveillance and risk-reduction strategies. Citation Format: Siddhartha Yadav, Nicholas J. Boddicker, Jie Na, Eric C. Polley, Chunling Hu, Steven N. Hart, Rohan D. Gnanaolivu, Nicole Larson, Carolyn Dunn, Susan Holtegaard, Huaizhi Huang, Lauren R. Teras, Alpa V. Patel, James V. Lacey Jr., Susan Neuhausen, Leslie Bernstein, Elena Martinez, Christopher Haiman, Fei Chen, Kathryn Ruddy, Janet Olson, Esther John, Allison W. Kurian, Dale P. Sandler, Katie M. O’Brien, Jack A. Taylor, Clarice R. Weinberg, Hoda Anton-Culver, Argyrios Ziogas, Gary R. Zirpoli, David E. Goldgar, Katherine L. Nathanson, Susan Domchek, Julie R. Palmer, Jeffrey Weitzel, Peter Kraft, Fergus J. Couch. Population-based Estimates of contralateral Breast Cancer Risk among Carriers of Germline Pathogenic Variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr GS4-04.
Annals of Oncology · 2022 · 430 citations
- Medicine
- Internal medicine
- Oncology
BACKGROUND: The randomized, double-blind OlympiA trial compared 1 year of the oral poly(adenosine diphosphate-ribose) polymerase inhibitor, olaparib, to matching placebo as adjuvant therapy for patients with pathogenic or likely pathogenic variants in germline BRCA1 or BRCA2 (gBRCA1/2pv) and high-risk, human epidermal growth factor receptor 2-negative, early breast cancer (EBC). The first pre-specified interim analysis (IA) previously demonstrated statistically significant improvement in invasive disease-free survival (IDFS) and distant disease-free survival (DDFS). The olaparib group had fewer deaths than the placebo group, but the difference did not reach statistical significance for overall survival (OS). We now report the pre-specified second IA of OS with updates of IDFS, DDFS, and safety. PATIENTS AND METHODS: One thousand eight hundred and thirty-six patients were randomly assigned to olaparib or placebo following (neo)adjuvant chemotherapy, surgery, and radiation therapy if indicated. Endocrine therapy was given concurrently with study medication for hormone receptor-positive cancers. Statistical significance for OS at this IA required P < 0.015. RESULTS: With a median follow-up of 3.5 years, the second IA of OS demonstrated significant improvement in the olaparib group relative to the placebo group [hazard ratio 0.68; 98.5% confidence interval (CI) 0.47-0.97; P = 0.009]. Four-year OS was 89.8% in the olaparib group and 86.4% in the placebo group (Δ 3.4%, 95% CI -0.1% to 6.8%). Four-year IDFS for the olaparib group versus placebo group was 82.7% versus 75.4% (Δ 7.3%, 95% CI 3.0% to 11.5%) and 4-year DDFS was 86.5% versus 79.1% (Δ 7.4%, 95% CI 3.6% to 11.3%), respectively. Subset analyses for OS, IDFS, and DDFS demonstrated benefit across major subgroups. No new safety signals were identified including no new cases of acute myeloid leukemia or myelodysplastic syndrome. CONCLUSION: With 3.5 years of median follow-up, OlympiA demonstrates statistically significant improvement in OS with adjuvant olaparib compared with placebo for gBRCA1/2pv-associated EBC and maintained improvements in the previously reported, statistically significant endpoints of IDFS and DDFS with no new safety signals.
Journal of the National Comprehensive Cancer Network · 2021 · 1110 citations
- Medicine
- Oncology
- Internal medicine
The NCCN Guidelines for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic focus primarily on assessment of pathogenic or likely pathogenic variants associated with increased risk of breast, ovarian, and pancreatic cancer and recommended approaches to genetic testing/counseling and management strategies in individuals with these pathogenic or likely pathogenic variants. This manuscript focuses on cancer risk and risk management for BRCA-related breast/ovarian cancer syndrome and Li-Fraumeni syndrome. Carriers of a BRCA1/2 pathogenic or likely pathogenic variant have an excessive risk for both breast and ovarian cancer that warrants consideration of more intensive screening and preventive strategies. There is also evidence that risks of prostate cancer and pancreatic cancer are elevated in these carriers. Li-Fraumeni syndrome is a highly penetrant cancer syndrome associated with a high lifetime risk for cancer, including soft tissue sarcomas, osteosarcomas, premenopausal breast cancer, colon cancer, gastric cancer, adrenocortical carcinoma, and brain tumors.
Adjuvant Olaparib for Patients with <i>BRCA1</i> - or <i>BRCA2</i> -Mutated Breast Cancer
New England Journal of Medicine · 2021 · 1627 citations
- Medicine
- Cancer research
- Oncology
BACKGROUND: germline mutation-associated early breast cancer. METHODS: germline pathogenic or likely pathogenic variants and high-risk clinicopathological factors who had received local treatment and neoadjuvant or adjuvant chemotherapy. Patients were randomly assigned (in a 1:1 ratio) to 1 year of oral olaparib or placebo. The primary end point was invasive disease-free survival. RESULTS: A total of 1836 patients underwent randomization. At a prespecified event-driven interim analysis with a median follow-up of 2.5 years, the 3-year invasive disease-free survival was 85.9% in the olaparib group and 77.1% in the placebo group (difference, 8.8 percentage points; 95% confidence interval [CI], 4.5 to 13.0; hazard ratio for invasive disease or death, 0.58; 99.5% CI, 0.41 to 0.82; P<0.001). The 3-year distant disease-free survival was 87.5% in the olaparib group and 80.4% in the placebo group (difference, 7.1 percentage points; 95% CI, 3.0 to 11.1; hazard ratio for distant disease or death, 0.57; 99.5% CI, 0.39 to 0.83; P<0.001). Olaparib was associated with fewer deaths than placebo (59 and 86, respectively) (hazard ratio, 0.68; 99% CI, 0.44 to 1.05; P = 0.02); however, the between-group difference was not significant at an interim-analysis boundary of a P value of less than 0.01. Safety data were consistent with known side effects of olaparib, with no excess serious adverse events or adverse events of special interest. CONCLUSIONS: pathogenic or likely pathogenic variants, adjuvant olaparib after completion of local treatment and neoadjuvant or adjuvant chemotherapy was associated with significantly longer survival free of invasive or distant disease than was placebo. Olaparib had limited effects on global patient-reported quality of life. (Funded by the National Cancer Institute and AstraZeneca; OlympiA ClinicalTrials.gov number, NCT02032823.).
A Population-Based Study of Genes Previously Implicated in Breast Cancer
New England Journal of Medicine · 2021 · 851 citations
- Genetics
- Biology
- Medicine
BACKGROUND: Population-based estimates of the risk of breast cancer associated with germline pathogenic variants in cancer-predisposition genes are critically needed for risk assessment and management in women with inherited pathogenic variants. METHODS: In a population-based case-control study, we performed sequencing using a custom multigene amplicon-based panel to identify germline pathogenic variants in 28 cancer-predisposition genes among 32,247 women with breast cancer (case patients) and 32,544 unaffected women (controls) from population-based studies in the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium. Associations between pathogenic variants in each gene and the risk of breast cancer were assessed. RESULTS: , were not associated with an increased risk of breast cancer. CONCLUSIONS: This study provides estimates of the prevalence and risk of breast cancer associated with pathogenic variants in known breast cancer-predisposition genes in the U.S. population. These estimates can inform cancer testing and screening and improve clinical management strategies for women in the general population with inherited pathogenic variants in these genes. (Funded by the National Institutes of Health and the Breast Cancer Research Foundation.).
Journal of Clinical Oncology · 2020 · 490 citations
- Medicine
- Internal medicine
- Oncology
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 benefit rate and progression-free survival (PFS). RESULTS: mutations alone. CONCLUSION: mutation carriers. These results emphasize the value of molecular characterization for treatment decisions in MBC.
Genetics in Medicine · 2020 · 58 citations
- Computer Science
- Political Science
- Data science
Technologies in genomic medicine have rapidly evolved and transformed the ability to deliver precision medicine in nearly every field of health care. As genomic medicine has advanced, the electronic health record (EHR) has simultaneously been adopted into routine practice. A recent Points to Consider Statement by the American College of Medical Genetics and Genomics (ACMG) provides a framework for the optimal integration of genomic data into the EHR.1.Grebe T.A. et al.The interface of genomic information with the electronic health record: a points to consider statement of the American College of Medical Genetics and Genomics (ACMG).10.1038/s41436-020-0841-2Genet. Med. 2020; 22: 1431-1436Google Scholar The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative effort including Penn Medicine clinicians, researchers, pathologists, legal staff, and information services with input and efforts from Epic Systems Corporation (Wisconsin) and Ambry Genetics Corporation (California), a commercial genetic testing laboratory. We describe our efforts to operationalize the ACMG guidelines in the “real world” to optimize our EHR (PennChart) for the delivery of precision medicine (Supplemental Fig. 1).INTEGRATION OF UNSTRUCTURED GENETIC DATA INTO THE EHRWe have taken a two-staged approach to integrating germline and somatic genetic data into the EHR. Currently, most genetic results are reported in unstructured PDF documents. We established common procedures across all Penn Medicine’s clinical genetics services for genetic testing reports, labeling them with a common naming convention and scanning them into a specific Genetic Results document type, which we created specifically for genetic testing results. This document filters both into our Lab (standard results) and PennChart Precision Medicine Tabs. We created the latter tab as a centralized location in the EHR to enable easy access to all genetic data, ensuring that it is not overlooked amid all the other testing that happens over a patient’s lifetime. This approach has standardized the real-time integration of unstructured genetic data into the EHR. Further, it has facilitated our efforts to import legacy data, as we began scanning all genetics documents with the common naming convention several years before implementing the Precision Medicine Tab.INTEGRATION OF DISCRETE GENETIC DATA INTO THE EHRAlthough the ACMG recommends that genetic results be incorporated into patient records, at minimum, as scanned PDF files or images, it is preferable to store them in discrete, computable format to enable electronic searching, clinical decision support (CDS), and secondary use for research and operations.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,3.Warner J.L. Jain S.K. Levy M.A. Integrating cancer genomic data into electronic health records.10.1186/s13073-016-0371-3Genome Med. 2016; 8Google Scholar The second stage of our efforts therefore aimed to integrate structured genetic data into the EHR. The PGI has leveraged Epic’s Genomics Module to record discrete genetic variant information in Human Genome Variation Society (HGVS) nomenclature along with the notation of significance (e.g., TP53 c.743G>A [p.Arg248Gln], pathogenic); transcript, genome build, chromosome, and genomic location are also included. Pharmacogenetic results are entered as diplotypes based on PharmVar star allele definitions (e.g., DPYD *1/*2A). Content experts throughout Penn Medicine collaborated to develop standard operating procedures (SOPs) to ensure institutional consistency for both manual and automated entry of discrete results into the Genomics Module. To date, these SOPs have been developed for autosomal dominant and pharmacogenetic variants with plans to expand to other result types over time, such as cytogenetics and autosomal recessive alleles. Manual entry of discrete genetic data into the Genomics Module is currently completed by genetics providers, who spend less than five minutes per variant.Interfacing directly with genetic testing laboratories is essential to move from manual to automated entry of discrete results into the EHR. The PGI partnered with Ambry Genetics to implement computerized order entry so providers can place genetic test orders directly into PennChart for import into the Ambry portal. The return of results has followed a phased approach using a Health Level 7 (HL7) interface, first with PDF reports and then with discrete results, which are automatically imported into the Genomics Module, linked to their associated PDF documents, and accompanied by direct provider notification. We are working on expanding our HL7 capacity with other commercial genetic testing laboratories.The ACMG also recommends that updated genetic test results be clearly linked to the original report in the EHR, as the interpretation of results may evolve and result in reclassifications over time.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar The PGI’s partnership with Ambry Genetics enables the automatic import of results both at the time of initial testing and as variant reclassification occurs. If an update occurs, a notification is sent to the ordering provider and care team for review of the amended report.LINKAGE OF GENETIC DATA TO CLINICAL DECISION SUPPORTOne of the greatest potential benefits of integrating genetic data into the EHR is the ability to link results to CDS.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,4.Hoffman M.A. The genome-enabled electronic medical record.1:CAS:528:DC%2BD28Xht1KrsbzP10.1016/j.jbi.2006.02.010J. Biomed. Informatics. 2007; 40: 44-46Google Scholar Not only should providers be able to retrieve external educational content to learn more about a patient’s genetic findings, but they should also receive automated recommendations at the point of care to facilitate clinical decision-making.5.Kawamoto K. Houlihan C.A. Balas E.A. Lobach D.F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.10.1136/bmj.38398.500764.8FBMJ. 2005; 330: 765Google Scholar,6.Overby C.L. et al.Opportunities for genomic clinical decision support interventions.10.1038/gim.2013.128Genet. Med. 2013; 15: 817-823Google Scholar The ACMG highlights that EHR vendors may not be fully equipped to build CDS systems in isolation due to the complex and dynamic nature of genomic medicine. The multidisciplinary nature of the PGI addresses this challenge by fostering collaboration between clinical, pathology, and information technology (IT) experts to build CDS tools that are seamlessly implemented into routine care.The PGI has leveraged Epic’s Genomic Indicators, driven by variants that are pathogenic/likely pathogenic or medically actionable, which are tags added to a patient’s record to indicate potential disease risk or drug sensitivity based on his/her genetic testing results. Genomic indicators are displayed on the Snapshot Tab (chart front page) and facilitate clinical decision-making by triggering automated recommendations directly in the EHR targeted to both providers and patients. By using triggered genomic indicators, we prevent variants of uncertain significance from being misinterpreted by nongenetics providers as disease associated. Our initial use cases for CDS provide guideline-concordant recommendations on colonoscopy timing intervals for patients with Lynch syndrome and fluoropyrimidine dose adjustments in patients with dihydropyrimidine dehydrogenase deficiency identified on DPYD gene testing.7.Natioanl Comprehensive Clinical Network, Clinical Practice Guidelines in Oncology. Genetic/Familial High-Risk Assesment: Colorectal, V 1.2020 - July 21, 2020. www.nccn.org/professionals/physician_gls/pdf/genetics_colon.pdf.Google Scholar,8.Amstutz U. et al.Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine TO GENETIC ACMG recommends that genetic data in the EHR be to patients in and the time of patients are that they receive their genetic results in with on their clinical and that may patients receive their results in As we have developed a to these data, as by patients are on their results, which ordering genetics providers them to nongenetics providers and patient portal. This electronic also features the Genetic a centralized location patients may their results, along with educational information in for genetic and pharmacogenetic our in the Genetic Results document to enable centralized document in the Precision Medicine Tab to patient it also provides the to genetic results from other EHR able to genetic data to be by the at our to provide the to that data for such as in health information use Genetic results from also are as a document the EHR so that they are only to genetics providers and be to external as of the patient’s PGI has in integrating genomic data into the EHR for the of patient care. To date, documents have been into the Precision Medicine including over legacy and discrete results from Ambry We our to to and efforts by with clinical, pathology, and legal throughout Penn our have not been The of genomic medicine educational to ensure that our multidisciplinary team has the to patient care. as we expand our discrete to other have the to with our EHR the PGI has from support and from Penn Medicine that may not be at As we are to our decision and other with the genomic medicine to efforts to optimize the integration of genomic data into the PGI SOPs autosomal dominant and pharmacogenetic variants for other types of genetic CDS systems for genetic test results, on genetic testing and risk with genetic testing laboratories for computerized order entry and discrete result with other to enable of genetic data for patients care at and and as genetic data more both and between We also to working with the genomic medicine to develop for such as genomic data variant reclassifications from external genetic results from and ensuring access to genomic medicine for all patients. The EHR is a for the delivery of precision we are that the efforts of the PGI and other be in patient care and the of genomic medicine over the of research from the University of is an of Ambry The other is by of Health and are by the for and are by is by is by the Technologies in genomic medicine have rapidly evolved and transformed the ability to deliver precision medicine in nearly every field of health care. As genomic medicine has advanced, the electronic health record (EHR) has simultaneously been adopted into routine practice. A recent Points to Consider Statement by the American College of Medical Genetics and Genomics (ACMG) provides a framework for the optimal integration of genomic data into the EHR.1.Grebe T.A. et al.The interface of genomic information with the electronic health record: a points to consider statement of the American College of Medical Genetics and Genomics (ACMG).10.1038/s41436-020-0841-2Genet. Med. 2020; 22: 1431-1436Google Scholar The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative effort including Penn Medicine clinicians, researchers, pathologists, legal staff, and information services with input and efforts from Epic Systems Corporation (Wisconsin) and Ambry Genetics Corporation (California), a commercial genetic testing laboratory. We describe our efforts to operationalize the ACMG guidelines in the “real world” to optimize our EHR (PennChart) for the delivery of precision medicine (Supplemental Fig. OF UNSTRUCTURED GENETIC DATA INTO THE EHRWe have taken a two-staged approach to integrating germline and somatic genetic data into the EHR. Currently, most genetic results are reported in unstructured PDF documents. We established common procedures across all Penn Medicine’s clinical genetics services for genetic testing reports, labeling them with a common naming convention and scanning them into a specific Genetic Results document type, which we created specifically for genetic testing results. This document filters both into our Lab (standard results) and PennChart Precision Medicine Tabs. We created the latter tab as a centralized location in the EHR to enable easy access to all genetic data, ensuring that it is not overlooked amid all the other testing that happens over a patient’s lifetime. This approach has standardized the real-time integration of unstructured genetic data into the EHR. Further, it has facilitated our efforts to import legacy data, as we began scanning all genetics documents with the common naming convention several years before implementing the Precision Medicine We have taken a two-staged approach to integrating germline and somatic genetic data into the EHR. Currently, most genetic results are reported in unstructured PDF documents. We established common procedures across all Penn Medicine’s clinical genetics services for genetic testing reports, labeling them with a common naming convention and scanning them into a specific Genetic Results document type, which we created specifically for genetic testing results. This document filters both into our Lab (standard results) and PennChart Precision Medicine Tabs. We created the latter tab as a centralized location in the EHR to enable easy access to all genetic data, ensuring that it is not overlooked amid all the other testing that happens over a patient’s lifetime. This approach has standardized the real-time integration of unstructured genetic data into the EHR. Further, it has facilitated our efforts to import legacy data, as we began scanning all genetics documents with the common naming convention several years before implementing the Precision Medicine OF DISCRETE GENETIC DATA INTO THE EHRAlthough the ACMG recommends that genetic results be incorporated into patient records, at minimum, as scanned PDF files or images, it is preferable to store them in discrete, computable format to enable electronic searching, clinical decision support (CDS), and secondary use for research and operations.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,3.Warner J.L. Jain S.K. Levy M.A. Integrating cancer genomic data into electronic health records.10.1186/s13073-016-0371-3Genome Med. 2016; 8Google Scholar The second stage of our efforts therefore aimed to integrate structured genetic data into the EHR. The PGI has leveraged Epic’s Genomics Module to record discrete genetic variant information in Human Genome Variation Society (HGVS) nomenclature along with the notation of significance (e.g., TP53 c.743G>A [p.Arg248Gln], pathogenic); transcript, genome build, chromosome, and genomic location are also included. Pharmacogenetic results are entered as diplotypes based on PharmVar star allele definitions (e.g., DPYD *1/*2A). Content experts throughout Penn Medicine collaborated to develop standard operating procedures (SOPs) to ensure institutional consistency for both manual and automated entry of discrete results into the Genomics Module. To date, these SOPs have been developed for autosomal dominant and pharmacogenetic variants with plans to expand to other result types over time, such as cytogenetics and autosomal recessive alleles. Manual entry of discrete genetic data into the Genomics Module is currently completed by genetics providers, who spend less than five minutes per variant.Interfacing directly with genetic testing laboratories is essential to move from manual to automated entry of discrete results into the EHR. The PGI partnered with Ambry Genetics to implement computerized order entry so providers can place genetic test orders directly into PennChart for import into the Ambry portal. The return of results has followed a phased approach using a Health Level 7 (HL7) interface, first with PDF reports and then with discrete results, which are automatically imported into the Genomics Module, linked to their associated PDF documents, and accompanied by direct provider notification. We are working on expanding our HL7 capacity with other commercial genetic testing laboratories.The ACMG also recommends that updated genetic test results be clearly linked to the original report in the EHR, as the interpretation of results may evolve and result in reclassifications over time.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar The PGI’s partnership with Ambry Genetics enables the automatic import of results both at the time of initial testing and as variant reclassification occurs. If an update occurs, a notification is sent to the ordering provider and care team for review of the amended the ACMG recommends that genetic results be incorporated into patient records, at minimum, as scanned PDF files or images, it is preferable to store them in discrete, computable format to enable electronic searching, clinical decision support (CDS), and secondary use for research and operations.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,3.Warner J.L. Jain S.K. Levy M.A. Integrating cancer genomic data into electronic health records.10.1186/s13073-016-0371-3Genome Med. 2016; 8Google Scholar The second stage of our efforts therefore aimed to integrate structured genetic data into the EHR. The PGI has leveraged Epic’s Genomics Module to record discrete genetic variant information in Human Genome Variation Society (HGVS) nomenclature along with the notation of significance (e.g., TP53 c.743G>A [p.Arg248Gln], pathogenic); transcript, genome build, chromosome, and genomic location are also included. Pharmacogenetic results are entered as diplotypes based on PharmVar star allele definitions (e.g., DPYD *1/*2A). Content experts throughout Penn Medicine collaborated to develop standard operating procedures (SOPs) to ensure institutional consistency for both manual and automated entry of discrete results into the Genomics Module. To date, these SOPs have been developed for autosomal dominant and pharmacogenetic variants with plans to expand to other result types over time, such as cytogenetics and autosomal recessive alleles. Manual entry of discrete genetic data into the Genomics Module is currently completed by genetics providers, who spend less than five minutes per directly with genetic testing laboratories is essential to move from manual to automated entry of discrete results into the EHR. The PGI partnered with Ambry Genetics to implement computerized order entry so providers can place genetic test orders directly into PennChart for import into the Ambry portal. The return of results has followed a phased approach using a Health Level 7 (HL7) interface, first with PDF reports and then with discrete results, which are automatically imported into the Genomics Module, linked to their associated PDF documents, and accompanied by direct provider notification. We are working on expanding our HL7 capacity with other commercial genetic testing The ACMG also recommends that updated genetic test results be clearly linked to the original report in the EHR, as the interpretation of results may evolve and result in reclassifications over time.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar The PGI’s partnership with Ambry Genetics enables the automatic import of results both at the time of initial testing and as variant reclassification occurs. If an update occurs, a notification is sent to the ordering provider and care team for review of the amended OF GENETIC DATA TO CLINICAL DECISION SUPPORTOne of the greatest potential benefits of integrating genetic data into the EHR is the ability to link results to CDS.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,4.Hoffman M.A. The genome-enabled electronic medical record.1:CAS:528:DC%2BD28Xht1KrsbzP10.1016/j.jbi.2006.02.010J. Biomed. Informatics. 2007; 40: 44-46Google Scholar Not only should providers be able to retrieve external educational content to learn more about a patient’s genetic findings, but they should also receive automated recommendations at the point of care to facilitate clinical decision-making.5.Kawamoto K. Houlihan C.A. Balas E.A. Lobach D.F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.10.1136/bmj.38398.500764.8FBMJ. 2005; 330: 765Google Scholar,6.Overby C.L. et al.Opportunities for genomic clinical decision support interventions.10.1038/gim.2013.128Genet. Med. 2013; 15: 817-823Google Scholar The ACMG highlights that EHR vendors may not be fully equipped to build CDS systems in isolation due to the complex and dynamic nature of genomic medicine. The multidisciplinary nature of the PGI addresses this challenge by fostering collaboration between clinical, pathology, and information technology (IT) experts to build CDS tools that are seamlessly implemented into routine care.The PGI has leveraged Epic’s Genomic Indicators, driven by variants that are pathogenic/likely pathogenic or medically actionable, which are tags added to a patient’s record to indicate potential disease risk or drug sensitivity based on his/her genetic testing results. Genomic indicators are displayed on the Snapshot Tab (chart front page) and facilitate clinical decision-making by triggering automated recommendations directly in the EHR targeted to both providers and patients. By using triggered genomic indicators, we prevent variants of uncertain significance from being misinterpreted by nongenetics providers as disease associated. Our initial use cases for CDS provide guideline-concordant recommendations on colonoscopy timing intervals for patients with Lynch syndrome and fluoropyrimidine dose adjustments in patients with dihydropyrimidine dehydrogenase deficiency identified on DPYD gene testing.7.Natioanl Comprehensive Clinical Network, Clinical Practice Guidelines in Oncology. Genetic/Familial High-Risk Assesment: Colorectal, V 1.2020 - July 21, 2020. www.nccn.org/professionals/physician_gls/pdf/genetics_colon.pdf.Google Scholar,8.Amstutz U. et al.Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine Scholar of the greatest potential benefits of integrating genetic data into the EHR is the ability to link results to CDS.2.Marsolo K. Spooner S.A. Clinical genomics in the world of the electronic health record.10.1038/gim.2013.88Genet. Med. 2013; 15: 786-791Google Scholar,4.Hoffman M.A. The genome-enabled electronic medical record.1:CAS:528:DC%2BD28Xht1KrsbzP10.1016/j.jbi.2006.02.010J. Biomed. Informatics. 2007; 40: 44-46Google Scholar Not only should providers be able to retrieve external educational content to learn more about a patient’s genetic findings, but they should also receive automated recommendations at the point of care to facilitate clinical decision-making.5.Kawamoto K. Houlihan C.A. Balas E.A. Lobach D.F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.10.1136/bmj.38398.500764.8FBMJ. 2005; 330: 765Google Scholar,6.Overby C.L. et al.Opportunities for genomic clinical decision support interventions.10.1038/gim.2013.128Genet. Med. 2013; 15: 817-823Google Scholar The ACMG highlights that EHR vendors may not be fully equipped to build CDS systems in isolation due to the complex and dynamic nature of genomic medicine. The multidisciplinary nature of the PGI addresses this challenge by fostering collaboration between clinical, pathology, and information technology (IT) experts to build CDS tools that are seamlessly implemented into routine care. The PGI has leveraged Epic’s Genomic Indicators, driven by variants that are pathogenic/likely pathogenic or medically actionable, which are tags added to a patient’s record to indicate potential disease risk or drug sensitivity based on his/her genetic testing results. Genomic indicators are displayed on the Snapshot Tab (chart front page) and facilitate clinical decision-making by triggering automated recommendations directly in the EHR targeted to both providers and patients. By using triggered genomic indicators, we prevent variants of uncertain significance from being misinterpreted by nongenetics providers as disease associated. Our initial use cases for CDS provide guideline-concordant recommendations on colonoscopy timing intervals for patients with Lynch syndrome and fluoropyrimidine dose adjustments in patients with dihydropyrimidine dehydrogenase deficiency identified on DPYD gene testing.7.Natioanl Comprehensive Clinical Network, Clinical Practice Guidelines in Oncology. Genetic/Familial High-Risk Assesment: Colorectal, V 1.2020 - July 21, 2020. www.nccn.org/professionals/physician_gls/pdf/genetics_colon.pdf.Google Scholar,8.Amstutz U. et al.Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine Scholar TO GENETIC ACMG recommends that genetic data in the EHR be to patients in and the time of patients are that they receive their genetic results in with on their clinical and that may patients receive their results in As we have developed a to these data, as by patients are on their results, which ordering genetics providers them to nongenetics providers and patient portal. This electronic also features the Genetic a centralized location patients may their results, along with educational information in for genetic and pharmacogenetic results. The ACMG recommends that genetic data in the EHR be to patients in and the time of patients are that they receive their genetic results in with on their clinical and that may patients receive their results in As we have developed a to these data, as by patients are on their results, which ordering genetics providers them to nongenetics providers and patient portal. This electronic also features the Genetic a centralized location patients may their results, along with educational information in for genetic and pharmacogenetic results. our in the Genetic Results document to enable centralized document in the Precision Medicine Tab to patient it also provides the to genetic results from other EHR able to genetic data to be by the at our to provide the to that data for such as in health information use Genetic results from also are as a document the EHR so that they are only to genetics providers and be to external as of the patient’s our in the Genetic Results document to enable centralized document in the Precision Medicine Tab to patient it also provides the to genetic results from other EHR able to genetic data to be by the at our to provide the to that data for such as in health information use Genetic results from also are as a document the EHR so that they are only to genetics providers and be to external as of the patient’s PGI has in integrating genomic data into the EHR for the of patient care. To date, documents have been into the Precision Medicine including over legacy and discrete results from Ambry We our to to and efforts by with clinical, pathology, and legal throughout Penn our have not been The of genomic medicine educational to ensure that our multidisciplinary team has the to patient care. as we expand our discrete to other have the to with our EHR the PGI has from support and from Penn Medicine that may not be at As we are to our decision and other with the genomic medicine to efforts to optimize the integration of genomic data into the PGI SOPs autosomal dominant and pharmacogenetic variants for other types of genetic CDS systems for genetic test results, on genetic testing and risk with genetic testing laboratories for computerized order entry and discrete result with other to enable of genetic data for patients care at and and as genetic data more both and between We also to working with the genomic medicine to develop for such as genomic data variant reclassifications from external genetic results from and ensuring access to genomic medicine for all patients. The EHR is a for the delivery of precision we are that the efforts of the PGI and other be in patient care and the of genomic medicine over The PGI has in integrating genomic data into the EHR for the of patient care. To date, documents have been into the Precision Medicine including over legacy and discrete results from Ambry We our to to and efforts by with clinical, pathology, and legal throughout Penn our have not been The of genomic medicine educational to ensure that our multidisciplinary team has the to patient care. as we expand our discrete to other have the to with our EHR the PGI has from support and from Penn Medicine that may not be at As we are to our decision and other with the genomic medicine to efforts to optimize the integration of genomic data into the EHR.
Journal of the American Heart Association · 2020 · 160 citations
- Medicine
- Internal medicine
- Cardiology
Background We examined the longitudinal associations between changes in cardiovascular biomarkers and cancer therapy-related cardiac dysfunction (CTRCD) in patients with breast cancer treated with cardotoxic cancer therapy. Methods and Results Repeated measures of high-sensitivity cardiac troponin T (hs-cTnT), NT-proBNP (N-terminal pro-B-type natriuretic peptide), myeloperoxidase, placental growth factor, and growth differentiation factor 15 were assessed longitudinally in a prospective cohort of 323 patients treated with anthracyclines and/or trastuzumab followed over a maximum of 3.7 years with serial echocardiograms. CTRCD was defined as a ≥10% decline in left ventricular ejection fraction to a value <50%. Associations between changes in biomarkers and left ventricular ejection fraction were evaluated in repeated-measures linear regression models. Cox regression models assessed the associations between biomarkers and CTRCD. Early increases in all biomarkers occurred with anthracycline-based regimens. hs-cTnT levels >14 ng/L at anthracycline completion were associated with a 2-fold increased CTRCD risk (hazard ratio, 2.01; 95% CI, 1.00-4.06). There was a modest association between changes in NT-proBNP and left ventricular ejection fraction in the overall cohort; this was most pronounced with sequential anthracycline and trastuzumab (1.1% left ventricular ejection fraction decline [95% CI, -1.8 to -0.4] with each NT-proBNP doubling). Increases in NT-proBNP were also associated with CTRCD (hazard ratio per doubling, 1.56; 95% CI, 1.32-1.84). Increases in myeloperoxidase were associated with CTRCD in patients who received sequential anthracycline and trastuzumab (hazard ratio per doubling, 1.28; 95% CI, 1.04-1.58). Conclusions Cardiovascular biomarkers may play an important role in CTRCD risk prediction in patients with breast cancer who receive cardiotoxic cancer therapy, particularly in those treated with sequential anthracycline and trastuzumab therapy. Clinical Trial Registration URL: https://www.clinicaltrials.gov/. Unique identifier: NCT01173341.
Genetics in Medicine · 2020 · 42 citations
- Medicine
- Internal medicine
- Oncology
PURPOSE: CTNNA1 is a potential diffuse gastric cancer risk gene, however CTNNA1 testing on multigene panel testing (MGPT) remains unstudied. METHODS: De-identified data from 151,425 individuals who underwent CTNNA1 testing at a commercial laboratory between October 2015 and July 2019 were reviewed. Tissue α-E-catenin immunohistochemistry was performed on CTNNA1 c.1351C>T (p.Arg451*) carriers. RESULTS: Fifty-two individuals (0.03% tested) had CTNNA1 loss-of-function (LOF) variants and 1057 individuals (0.7% tested) had a total of 302 distinct missense variants of uncertain significance. Detailed history was available on 33 CTNNA1 LOF carriers, with 21 unique CTNNA1 LOF variants. Four (12%) individuals had diffuse gastric cancer and 22 (67%) had breast cancer. Six (21%) and 24 (83%) of the 29 families reported a history of gastric or breast cancer, respectively. The CTNNA1 c.1351C>T nonsense variant was identified in three separate families with early-onset diffuse gastric cancer or breast cancer. Immunohistochemistry showed decreased α-E-catenin expression in gastric cancers. CONCLUSION: CTNNA1 LOF variants are detected on MGPT with a majority of these individuals having gastric or breast cancer. The overall risk of gastric cancer for CTNNA1 LOF carriers may be lower than expected. Given the uncertain phenotype and penetrance, management of individuals with CTNNA1 LOF variants remains challenging.
Recent grants
Radiobiology and Imaging Program
NIH · $93.0M · 1997–2027
Frequent coauthors
- 1012 shared
Katherine L. Nathanson
University of Pennsylvania
- 804 shared
Fergus J. Couch
Mayo Clinic in Arizona
- 490 shared
Susan L. Neuhausen
- 483 shared
Olufunmilayo I. Olopade
- 472 shared
Jeffrey N. Weitzel
The University of Kansas Cancer Center
- 435 shared
Paolo Radice
- 425 shared
D. Gareth Evans
The Christie NHS Foundation Trust
- 419 shared
Mark E. Robson
Cornell University
Labs
Susan M Domchek LabPI
Education
- 1990
B.A., Engineering Sciences
Dartmouth College
- 1995
M.D., Internal Medicine
Harvard Medical School
Awards & honors
- Basser Professor in Oncology
- Director, Cancer Risk Evaluation Program, Abramson Cancer Ce…
- Senior Fellow, Leonard Davis Institute of Health Economics,…
- Executive Director, Basser Center for BRCA
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