Racial Differences in Cutaneous Events Among Patients Receiving Enfortumab Vedotin.

INTRODUCTION: Enfortumab vedotin (EV) is an antibody-drug conjugate approved alone and in combination with pembrolizumab for advanced urothelial cancer (UC). EV-related-cutaneous-events (EVCEs) are common and rarely life-threatening. Black patients are frequently under-represented in oncology trials, and dermatologic conditions may vary with race. METHODS: Therefore, this retrospective analysis investigated differences in EVCE frequency between Black and White patients in an urban cohort (Johns Hopkins [JH]) and a US-based, nationwide electronic health record (EHR)-derived deidentified database (Flatiron Health [FH]) with sub-group analysis of those who had received prior pembrolizumab. RESULTS: The study included 12 Black patients in the JH Cohort (17.1%) and 24 Black patients in the FH Cohort (7.6%). In both cohorts, the frequency of EVCEs among Black patients was higher compared to White patients (JH: 66.7% vs. 33.3%; FH: 25.0% vs. 15.8%), though not statistically significant. In the larger FH Cohort EVCEs were significantly more common among Black compared to White patients treated with prior pembrolizumab (Odds Ratio [OR]: 4.76 [95%CI: 1.42, 15.95]) and recent pembrolizumab (within 90 days of EV initiation) (OR 9.00 [95%CI: 1.94, 41.66]). CONCLUSION: This hypothesis-generating retrospective study, comprising the largest population of EV-treated Black patients reported to date, emphasizes the importance of attentiveness to EVCEs among Black patients, particularly with receipt of pembrolizumab.

Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.

Extracellular Vesicle-Based Multianalyte Liquid Biopsy as a Diagnostic for Cancer.

Liquid biopsy is the analysis of materials shed by tumors into circulation, such as circulating tumor cells, nucleic acids, and extracellular vesicles (EVs), for the diagnosis and management of cancer. These assays have rapidly evolved with recent FDA approvals of single biomarkers in patients with advanced metastatic disease. However, they have lacked sensitivity or specificity as a diagnostic in early-stage cancer, primarily due to low concentrations in circulating plasma. EVs, membrane-enclosed nanoscale vesicles shed by tumor and other cells into circulation, are a promising liquid biopsy analyte owing to their protein and nucleic acid cargoes carried from their mother cells, their surface proteins specific to their cells of origin, and their higher concentrations over other noninvasive biomarkers across disease stages. Recently, the combination of EVs with non-EV biomarkers has driven improvements in sensitivity and accuracy; this has been fueled by the use of machine learning (ML) to algorithmically identify and combine multiple biomarkers into a composite biomarker for clinical prediction. This review presents an analysis of EV isolation methods, surveys approaches for and issues with using ML in multianalyte EV datasets, and describes best practices for bringing multianalyte liquid biopsy to clinical implementation.

Plasma Genotyping at the Time of Diagnostic Tissue Biopsy Decreases Time-to-Treatment in Patients With Advanced NSCLC-Results From a Prospective Pilot Study.

Introduction: The availability of targeted therapies has transformed the management of advanced NSCLC; however, most patients do not undergo guideline-recommended tumor genotyping. The impact of plasma-based next-generation sequencing (NGS) performed simultaneously with diagnostic biopsy in suspected advanced NSCLC has largely been unexplored. Methods: We performed a prospective cohort study of patients with suspected advanced lung cancer on the basis of cross-sectional imaging results. Blood from the time of biopsy was sequenced using a commercially available 74-gene panel. The primary outcome measure was time to first-line systemic treatment compared with a retrospective cohort of consecutive patients with advanced NSCLC with reflex tissue NGS. Results: We analyzed the NGS results from 110 patients with newly diagnosed advanced NSCLC: cohorts 1 and 2 included 55 patients each and were well balanced regarding baseline demographics. In cohort 1, plasma NGS identified therapeutically informative driver mutations in 32 patients (58%) (13 KRAS [five KRAS G12C], 13 EGFR, two ERRB2, two MET, one BRAF, one RET). The NGS results were available before the first oncology visit in 85% of cohort 1 versus 9% in cohort 2 (p < 0.0001), with more cohort 1 patients receiving a guideline-concordant treatment recommendation at this visit (74% versus 46%, p = 0.005). Time-to-treatment was significantly shorter in cohort 1 compared with cohort 2 (12 versus 20 d, p = 0.003), with a shorter time-to-treatment in patients with specific driver mutations (10 versus 19 d, p = 0.001). Conclusions: Plasma-based NGS performed at the time of diagnostic biopsy in patients with suspected advanced NSCLC is associated with decreased time-to-treatment compared with usual care.

Racial Disparities in 30-Day Outcomes Following Index Admission for COVID-19.

We investigated racial disparities in a 30-day composite outcome of readmission and death among patients admitted across a 5-hospital health system following an index COVID-19 admission. A dataset of 1,174 patients admitted between March 1, 2020 and August 21, 2020 for COVID-19 was retrospectively analyzed for odds of readmission among Black patients compared to all other patients, with sequential adjustment for demographics, index admission characteristics, type of post-acute care, and comorbidities. Tabulated results demonstrated a significantly greater odds of 30-day readmission or death among Black patients (18.0% of Black patients vs. 11.3% of all other patients; Univariate Odds Ratio: 1.71, p = 0.002). Sequential adjustment via logistic regression revealed that the odds of 30-day readmission or death were significantly greater among Black patients after adjustment for demographics, index admission characteristics, and type of post-acute care, but not comorbidities. Stratification by type of post-acute care received on discharge revealed that the same disparity in odds of 30-day readmission or death existed among patients discharged home without home services, but not those discharged to home with home services or to a skilled nursing facility or acute rehab facility. Collectively, the findings suggest that weighing comorbidity burdens in post-acute care decisions may be relevant in addressing racial disparities in 30-day outcomes following discharge from an index COVID-19 admission.

Impact of telemedicine adoption on accessibility and time to treatment in patients with thoracic malignancies during the COVID-19 pandemic.

BACKGROUND: To ensure safe delivery of oncologic care during the COVID-19 pandemic, telemedicine has been rapidly adopted. However, little data exist on the impact of telemedicine on quality and accessibility of oncologic care. This study assessed whether conducting an office visit for thoracic oncology patients via telemedicine affected time to treatment initiation and accessibility. METHODS: This was a retrospective cohort study of patients with thoracic malignancies seen by a multidisciplinary team during the first surge of COVID-19 cases in Philadelphia (March 1 to June 30, 2020). Patients with an index visit for a new phase of care, defined as a new diagnosis, local recurrence, or newly discovered metastatic disease, were included. RESULTS: 240 distinct patients with thoracic malignancies were seen: 132 patients (55.0%) were seen initially in-person vs 108 (45.0%) via telemedicine. The majority of visits were for a diagnosis of a new thoracic cancer (87.5%). Among newly diagnosed patients referred to the thoracic oncology team, the median time from referral to initial visit was significantly shorter amongst the patients seen via telemedicine vs. in-person (median 5.0 vs. 6.5 days, p < 0.001). Patients received surgery (32.5%), radiation (24.2%), or systemic therapy (30.4%). Time from initial visit to treatment initiation by modality did not differ by telemedicine vs in-person: surgery (22 vs 16 days, p = 0.47), radiation (27.5 vs 27.5 days, p = 0.86, systemic therapy (15 vs 13 days, p = 0.45). CONCLUSIONS: Rapid adoption of telemedicine allowed timely delivery of oncologic care during the initial surge of the COVID19 pandemic by a thoracic oncology multi-disciplinary clinic.

Views From Patients With Cancer in the Setting of Unplanned Acute Care: Informing Approaches to Reduce Health Care Utilization.

PURPOSE: New oncology care delivery models that avoid preventable acute care are needed, yet it is unclear which interventions best meet the needs of patients and caregivers. Perspectives from patients who experienced unplanned acute care events may inform the successful development and implementation of care delivery models. METHODS: We performed a qualitative interview study of patients with solid tumors on active treatment who experienced the following 3 types of unplanned acute care events: emergency department visits, first hospitalizations, and multiple hospitalizations. Patients were prospectively recruited within a large academic health system from August 2018 to January 2019. Interviews followed a semi-structured guide developed from the Consolidated Framework for Implementation Research. The constant comparative approach was used to identify themes. RESULTS: Forty-nine patients were interviewed; 51% were men, 75% were non-Hispanic White, and the mean age was 57.4 years (standard deviation, 1.9 years). Fifty-five percent of patients had metastatic disease, and 33% had an Eastern Cooperative Oncology Group performance status of 3-4. We identified the following key themes: drivers of the decision to seek acute care, patients' emotional concerns that influence interactions with the oncology team, and strategies used to avoid acute care. Patients' recommendations for interventions included anticipatory guidance, peer support, improved triage methods, and enhanced symptom management. Patients preferred options for virtual and home-based outpatient care. CONCLUSION: Patient-centered care models should focus on early delivery of supportive interventions that help patients and caregivers navigate the unexpected issues that come with cancer treatment. Patients advocate for proactive, multidisciplinary supportive interventions that enable home-based care and are led by the primary oncology team.

Treatments Administered to the First 9152 Reported Cases of COVID-19: A Systematic Review.

The emergence of SARS-CoV-2/2019 novel coronavirus (COVID-19) has created a global pandemic with no approved treatments or vaccines. Many treatments have already been administered to COVID-19 patients but have not been systematically evaluated. We performed a systematic literature review to identify all treatments reported to be administered to COVID-19 patients and to assess time to clinically meaningful response for treatments with sufficient data. We searched PubMed, BioRxiv, MedRxiv, and ChinaXiv for articles reporting treatments for COVID-19 patients published between 1 December 2019 and 27 March 2020. Data were analyzed descriptively. Of the 2706 articles identified, 155 studies met the inclusion criteria, comprising 9152 patients. The cohort was 45.4% female and 98.3% hospitalized, and mean (SD) age was 44.4 years (SD 21.0). The most frequently administered drug classes were antivirals, antibiotics, and corticosteroids, and of the 115 reported drugs, the most frequently administered was combination lopinavir/ritonavir, which was associated with a time to clinically meaningful response (complete symptom resolution or hospital discharge) of 11.7 (1.09) days. There were insufficient data to compare across treatments. Many treatments have been administered to the first 9152 reported cases of COVID-19. These data serve as the basis for an open-source registry of all reported treatments given to COVID-19 patients at www.CDCN.org/CORONA . Further work is needed to prioritize drugs for investigation in well-controlled clinical trials and treatment protocols.

Gamma-Glutamyltransferase 1 Promotes Clear Cell Renal Cell Carcinoma Initiation and Progression.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer. While the localized form of this disease can be treated surgically, advanced and metastatic stages are resistant to chemotherapies. Although more innovative treatments, such as targeted or immune-based therapies, exist, the need for new therapeutic options remains. ccRCC presents unique metabolic signatures and multiple studies have reported a significant increase in levels of reduced glutathione (GSH) and its precursors in ccRCC tumor samples compared with normal kidney tissues. These observations led us to investigate the effects of blocking the GSH pathway, particularly the gamma-glutamyltransferase 1 (GGT1) enzyme, in multiple ccRCC cell lines. In this study, we provide in vitro and in vivo evidence that GGT1/GSH pathway inhibition impacts ccRCC cell growth, through increased cell-cycle arrest. Of note, GGT1 inhibition also impairs ccRCC cell migration. Finally, pharmacologic GSH pathway inhibition decreases ccRCC cell proliferation and increases sensitivity to standard chemotherapy. Our results suggest that GGT1/GSH pathway inhibition represents a new strategy to overcome ccRCC chemoresistance. IMPLICATIONS: GGT1/GSH pathway inhibition represents a promising therapeutic strategy to overcome chemoresistance and inhibit progression of ccRCC tumors.

Medical employment growth, unemployment, and the opportunity cost of health care.

This policy note examines the relationship between the growth in the share of the workforce in medical care and the shares of workers who are unemployed, working in services or government employment, or working elsewhere in the economy. These changes provide measures of the opportunity cost of higher medical care spending, the majority of which is on labor. Using state data over the period 1990-2010, we find that, in years of high economy-wide unemployment, growth in medical employment in a state reduces the unemployment rate significantly; it does not appear to displace employment in other services or government employment. In periods of low economy wide-unemployment, the growth in the medical employment share does not reduce unemployment. We argue that the opportunity cost of higher medical care employment may sometimes not be so high in terms of real labor resources, nor in terms of employment for needed government services.

Comprehensive integrated care for patients with sickle cell disease in a remote aboriginal tribal population in southern India.

BACKGROUND: Healthcare delivery for sickle cell disease (SCD) can be challenging, in low resource settings. We studied the feasibility of delivering comprehensive SCD care in a community-based network for remote, economically, and socially disadvantaged tribes in Gudalur, India. PROCEDURE: We reviewed medical records for all patients followed at the Gudalur Adivasi Hospital. We used published quality of care indicators to benchmark screening and routine healthcare maintenance. RESULTS: We screened 9,646 individuals (60.4%) under the age of 30 of a population of 25,000 individuals. Of 111 active patients with SCD, 71% have had at least one annual comprehensive clinic visit at a median visit interval of 57 days. We provided pneumococcal immunization and penicillin prophylaxis to 56 (50%) patients and HU to 68 (61%). Median spleen size was 1 cm (range 1-6 cm), mean was Hb 9.3 g/dl and we reported a mean of 0.7 painful episodes/year. Premature deaths occurred in 19 patients at a median age of 23 years due to acute chest syndrome, sepsis, severe anemia, stroke, mesenteric infarction, puerperal sepsis, or sudden unexplained death. Healthcare maintenance met 11 of 17 published SCD quality of care indicators. Average cost was 1,343 Indian Rupees (INR) (approximately US$ 25) per hospitalization and 173 INR (approximately US$ 4) per clinic visit. CONCLUSION: High quality comprehensive care for SCD can be delivered for a low income, aboriginal population in India through a community driven network of care. This model can serve as a template for healthcare delivery for SCD in low-income communities.