Faculty Bibliography

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

PURPOSE/OBJECTIVE:To explore whether a machine-learning algorithm could accurately perform the initial screening of medical school applications. METHOD/METHODS:Using application data and faculty screening outcomes from the 2013 to 2017 application cycles (n = 14,555 applications), the authors created a virtual faculty screener algorithm. A retrospective validation using 2,910 applications from the 2013 to 2017 cycles and a prospective validation using 2,715 applications during the 2018 application cycle were performed. To test the validated algorithm, a randomized trial was performed in the 2019 cycle, with 1,827 eligible applications being reviewed by faculty and 1,873 by algorithm. RESULTS:The retrospective validation yielded area under the receiver operating characteristic (AUROC) values of 0.83, 0.64, and 0.83 and area under the precision-recall curve (AUPRC) values of 0.61, 0.54, and 0.65 for the invite for interview, hold for review, and reject groups, respectively. The prospective validation yielded AUROC values of 0.83, 0.62, and 0.82 and AUPRC values of 0.66, 0.47, and 0.65 for the invite for interview, hold for review, and reject groups, respectively. The randomized trial found no significant differences in overall interview recommendation rates according to faculty or algorithm and among female or underrepresented in medicine applicants. In underrepresented in medicine applicants, there were no significant differences in the rates at which the admissions committee offered an interview (70 of 71 in the faculty reviewer arm and 61 of 65 in the algorithm arm; P = .14). No difference in the rate of the committee agreeing with the recommended interview was found among female applicants (224 of 229 in the faculty reviewer arm and 220 of 227 in the algorithm arm; P = .55). CONCLUSIONS:The virtual faculty screener algorithm successfully replicated faculty screening of medical school applications and may aid in the consistent and reliable review of medical school applicants.

Our previous studies identified a population of stem cell-like proliferating myeloid cells within inflamed tissues that could serve as a reservoir for tissue macrophages to adopt different activation states depending on the microenvironment. By lineage-tracing cells derived from CX3CR1+ precursors in mice during infection and profiling by single-cell RNA sequencing, in this study, we identify a cluster of BIRC5+ myeloid cells that expanded in the liver during chronic infection with either the parasite Schistosoma mansoni or the bacterial pathogen Staphylococcus aureus. In the absence of tissue-damaging toxins, S. aureus infection does not elicit these BIRC5+ cells. Moreover, deletion of BIRC5 from CX3CR1-expressing cells results in improved survival during S. aureus infection. Hence the combination of single-cell RNA sequencing and genetic fate-mapping CX3CR1+ cells revealed a toxin-dependent pathogenic role for BIRC5 in myeloid cells during S. aureus infection.

Both SARS-CoV-2 infection and vaccination elicit potent immune responses. A number of studies have described immune responses to SARS-CoV-2 infection. However, beyond antibody production, immune responses to COVID-19 vaccines remain largely uncharacterized. Here, we performed multimodal single-cell sequencing on peripheral blood of patients with acute COVID-19 and healthy volunteers before and after receiving the SARS-CoV-2 BNT162b2 mRNA vaccine to compare the immune responses elicited by the virus and by this vaccine. Phenotypic and transcriptional profiling of immune cells, coupled with reconstruction of the B and T cell antigen receptor rearrangement of individual lymphocytes, enabled us to characterize and compare the host responses to the virus and to defined viral antigens. While both infection and vaccination induced robust innate and adaptive immune responses, our analysis revealed significant qualitative differences between the two types of immune challenges. In COVID-19 patients, immune responses were characterized by a highly augmented interferon response which was largely absent in vaccine recipients. Increased interferon signaling likely contributed to the observed dramatic upregulation of cytotoxic genes in the peripheral T cells and innate-like lymphocytes in patients but not in immunized subjects. Analysis of B and T cell receptor repertoires revealed that while the majority of clonal B and T cells in COVID-19 patients were effector cells, in vaccine recipients clonally expanded cells were primarily circulating memory cells. Importantly, the divergence in immune subsets engaged, the transcriptional differences in key immune populations, and the differences in maturation of adaptive immune cells revealed by our analysis have far-ranging implications for immunity to this novel pathogen.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Medical schools and residency programs are increasingly incorporating personalization of content, pathways, and assessments to align with a competency-based model. Yet, such efforts face challenges involving large amounts of data, sometimes struggling to deliver insights in a timely fashion for trainees, coaches, and programs. In this article, the authors argue that the emerging paradigm of precision medical education (PME) may ameliorate some of these challenges. However, PME lacks a widely accepted definition and a shared model of guiding principles and capacities, limiting widespread adoption. The authors propose defining PME as a systematic approach that integrates longitudinal data and analytics to drive precise educational interventions that address each individual learner's needs and goals in a continuous, timely, and cyclical fashion, ultimately improving meaningful educational, clinical, or system outcomes. Borrowing from precision medicine, they offer an adapted shared framework. In the P4 medical education framework, PME should (1) take a proactive approach to acquiring and using trainee data; (2) generate timely personalized insights through precision analytics (including artificial intelligence and decision-support tools); (3) design precision educational interventions (learning, assessment, coaching, pathways) in a participatory fashion, with trainees at the center as co-producers; and (4) ensure interventions are predictive of meaningful educational, professional, or clinical outcomes. Implementing PME will require new foundational capacities: flexible educational pathways and programs responsive to PME-guided dynamic and competency-based progression; comprehensive longitudinal data on trainees linked to educational and clinical outcomes; shared development of requisite technologies and analytics to effect educational decision-making; and a culture that embraces a precision approach, with research to gather validity evidence for this approach and development efforts targeting new skills needed by learners, coaches, and educational leaders. Anticipating pitfalls in the use of this approach will be important, as will ensuring it deepens, rather than replaces, the interaction of trainees and their coaches.

BACKGROUND:Psoriasis is an inflammatory skin disease associated with increased cardiovascular (CV) risk, whose pathogenesis is not fully known. OBJECTIVE:We identified a transcriptomic signature in psoriasis and investigated its association with prevalent and future risk of a CV event to understand the connection between psoriasis and CV disease (CVD). METHODS:Psoriasis patients (n = 37) with a history of moderate-severe skin disease without CVD and 11 matched controls underwent whole blood RNA sequencing. This transcriptomic signature in psoriasis versus controls was evaluated in two CVD cohorts: Women referred for cardiac catheterization with (n = 76) versus without (n = 97) myocardial infarction (MI), and patients with peripheral artery disease (n = 106) followed over 2.5 years for major adverse CV or limb events (MACLE). The association between genes differentially expressed in psoriasis and prevalent and incident CV events was assed. RESULTS:In psoriasis, median age was 44 (IQR; 34-51) years, 49% male and ACC/AHA ASCVD Risk Score of 1.0% (0.6-3.4) with no significant difference versus controls. The median psoriasis area and severity index score (PASI) was 4.0 (IQR 2.9-8.2) with 36% on biologic therapy. Overall, 247 whole blood genes were upregulated and 228 downregulated in psoriasis versus controls (p < 0.05), and 1302 genes positively and 1244 genes negatively correlated with PASI (p < 0.05). Seventy-three genes overlapped between psoriasis prevalence and PASI with key regulators identified as IL-6, IL-1β and interferon gamma. In the CVD cohorts, 50 of 73 genes (68%) identified in psoriasis associated with prevalent MI, and 29 (40%) with incident MACLE. Key regulator transcripts identified in psoriasis and CVD cohorts included SOCS3, BCL3, OSM, PIM2, PIM3 and STAT5A. CONCLUSIONS:A whole blood transcriptomic signature of psoriasis diagnosis and severity associated with prevalent MI and incident MACLE. These data have implications for better understanding the link between psoriasis, systemic inflammation and CVD.

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies (1, 2). Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers (3-10). Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness this data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites (11, 12) are only experimentally supported in a small number of substrate sets (13, 14). To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

PROBLEM:Ability to set goals and work with coaches can support individualized, self-directed learning. Understanding the focus and quality of graduating medical student and first-year resident goals and the influence of coaching on goal-setting can inform efforts to support learners through the transition from medical school to residency. APPROACH:This observational study examined goal-setting among graduating medical students and first-year residents from April 2021 to March 2022. The medical students set goals while participating in a Transition to Residency elective. The residents in internal medicine, obstetrics and gynecology, emergency medicine, orthopedics, and pathology set goals through meeting 1:1 with coaches. Raters assessed goals using a 3-point rubric on domains of specific, measurable, attainable, relevant, and timely (i.e., SMART goal framework) and analyzed descriptive statistics, Mann-Whitney U tests, and linear regressions. OUTCOMES:Among 48 medical students, 30 (62.5%) set 108 goals for early residency. Among 134 residents, 62 (46.3%) entered goals. Residents met with coaches 2.8 times on average (range 0-8 meetings, median = 3). Goal quality was higher in residents than medical students (average score for S: 2.71 vs 2.06, P < .001; M: 2.38 vs 1.66, P < .001; A: 2.92 vs 2.64, P < .001; R: 2.94 vs 2.86, P = .002; T: 1.71 vs 1.31, P < .001). The number of coaching meetings was associated with more specific, measurable goals (specific: F [1, 1.02] = 6.56, P = .01, R2 = .10; measurable: F [1, 1.49] = 4.74, P = .03, R2 = .07). NEXT STEPS:Learners set realistic, attainable goals through the transition to residency, but the goals could be more specific, measurable, and timely. The residents set SMARTer goals, with coaching improving goal quality. Understanding how best to scaffold coaching and support goal-setting through this transition may improve trainees' self-directed learning and well-being.