Persistent Child Poverty and Mortality in a Cohort of Children with Cancer in Alabama.

BACKGROUND: One-fifth of U.S. counties are designated persistent child poverty counties (≥20% of children in poverty since 1980). The association between a persistent child poverty environment and mortality in children with cancer is unknown. METHODS: Our cohort includes 2,089 children with cancer (2000-2016) in Alabama. We used multivariable Cox proportional hazards modeling (adjusted for sociodemographics/clinical characteristics) to assess mortality by persistent child poverty designation at 1, 5, and 10 years from diagnosis. Distance to treatment was subsequently explored. RESULTS: Forty-two percent of the cohort lived in a persistent child poverty county; they were more likely to be African American (P < 0.0001), have public/no insurance (P = 0.0009), and live >100 miles to treatment (P < 0.0001). Children in persistent child poverty counties were 30% more likely to die by 5 years [95% confidence interval (CI) = 1.06-1.59; P = 0.012]. Distance (per 20-mile increase) to treatment was associated with a 9% increased mortality risk (P < 0.0001). Children with both exposures (distance >100 miles and persistent child poverty) faced the highest mortality risk at 5 years (HR = 1.80; 95% CI = 1.39-2.33; P < 0.0001). In subanalysis, children exposed to persistent child poverty were at higher risk for cancer-related mortality. However, the risk of health-related mortality did not differ. CONCLUSIONS: Among children with cancer from the Deep South, persistent child poverty was a prevalent exposure associated with inferior overall survival. Distance to treatment was independently associated with inferior survival. Children with both exposures had the highest risk of mortality. IMPACT: Persistent child poverty is associated with inferior survival among children with cancer; mechanisms underlying this disparity warrant investigation. See related commentary by Orjuela-Grimm and Beauchemin, p. 295.

Rates of laboratory adverse events by course in paediatric leukaemia ascertained with automated electronic health record extraction: a retrospective cohort study from the Children's Oncology Group.

BACKGROUND: Adverse events are often misreported in clinical trials, leading to an incomplete understanding of toxicities. We aimed to test automated laboratory adverse event ascertainment and grading (via the ExtractEHR automated package) to assess its scalability and define adverse event rates for children with acute myeloid leukaemia and acute lymphoblastic leukaemia. METHODS: For this retrospective cohort study from the Children's Oncology Group (COG), we included patients aged 0-22 years treated for acute myeloid leukaemia or acute lymphoblastic leukaemia at Children's Healthcare of Atlanta (Atlanta, GA, USA) from Jan 1, 2010, to Nov 1, 2018, at the Children's Hospital of Philadelphia (Philadelphia, PA, USA) from Jan 1, 2011, to Dec 31, 2014, and at the Texas Children's Hospital (Houston, TX, USA) from Jan 1, 2011, to Dec 31, 2014. The ExtractEHR automated package acquired, cleaned, and graded laboratory data as per Common Terminology Criteria for Adverse Events (CTCAE) version 5 for 22 commonly evaluated grade 3-4 adverse events (fatal events were not evaluated) with numerically based CTCAE definitions. Descriptive statistics tabulated adverse event frequencies. Adverse events ascertained by ExtractEHR were compared to manually reported adverse events for patients enrolled in two COG trials (AAML1031, NCT01371981; AALL0932, NCT02883049). Analyses were restricted to protocol-defined chemotherapy courses (induction I, induction II, intensification I, intensification II, and intensification III for acute myeloid leukaemia; induction, consolidation, interim maintenance, delayed intensification, and maintenance for acute lymphoblastic leukaemia). FINDINGS: Laboratory adverse event data from 1077 patients (583 from Children's Healthcare of Atlanta, 200 from the Children's Hospital of Philadelphia, and 294 from the Texas Children's Hospital) who underwent 4611 courses (549 for acute myeloid leukaemia and 4062 for acute lymphoblastic leukaemia) were extracted, processed, and graded. Of the 166 patients with acute myeloid leukaemia, 86 (52%) were female, 80 (48%) were male, 96 (58%) were White, and 132 (80%) were non-Hispanic. Of the 911 patients with acute lymphoblastic leukaemia, 406 (45%) were female, 505 (55%) were male, 596 (65%) were White, and 641 (70%) were non-Hispanic. Patients with acute myeloid leukaemia had the most adverse events during induction I and intensification II. Hypokalaemia (one [17%] of six to 75 [48%] of 156 courses) and alanine aminotransferase (ALT) increased (13 [10%] of 134 to 27 [17%] of 156 courses) were the most prevalent non-haematological adverse events in patients with acute myeloid leukaemia, as identified by ExtractEHR. Patients with acute lymphoblastic leukaemia had the greatest number of adverse events during induction and maintenance (eight adverse events with prevalence ≥10%; induction and maintenance: anaemia, platelet count decreased, white blood cell count decreased, neutrophil count decreased, lymphocyte count decreased, ALT increased, and hypocalcaemia; induction: hypokalaemia; maintenance: aspartate aminotransferase [AST] increased and blood bilirubin increased), as identified by ExtractEHR. 187 (85%) of 220 total comparisons in 22 adverse events in four AAML1031 and six AALL0923 courses were substantially higher with ExtractEHR than COG-reported adverse event rates for adverse events with a prevalence of at least 2%. INTERPRETATION: ExtractEHR is scalable and accurately defines laboratory adverse event rates for paediatric acute leukaemia; moreover, ExtractEHR seems to detect higher rates of laboratory adverse events than those reported in COG trials. These rates can be used for comparisons between therapies and to counsel patients treated on or off trials about the risks of chemotherapy. ExtractEHR-based adverse event ascertainment can improve reporting of laboratory adverse events in clinical trials. FUNDING: US National Institutes of Health, St Baldrick's Foundation, and Alex's Lemonade Stand Foundation.

Extensive Remodeling of the Immune Microenvironment in B Cell Acute Lymphoblastic Leukemia.

A subset of B cell acute lymphoblastic leukemia (B-ALL) patients will relapse and succumb to therapy-resistant disease. The bone marrow microenvironment may support B-ALL progression and treatment evasion. Utilizing single-cell approaches, we demonstrate B-ALL bone marrow immune microenvironment remodeling upon disease initiation and subsequent re-emergence during conventional chemotherapy. We uncover a role for non-classical monocytes in B-ALL survival, and demonstrate monocyte abundance at B-ALL diagnosis is predictive of pediatric and adult B-ALL patient survival. We show that human B-ALL blasts alter a vascularized microenvironment promoting monocytic differentiation, while depleting leukemia-associated monocytes in B-ALL animal models prolongs disease remission in vivo. Our profiling of the B-ALL immune microenvironment identifies extrinsic regulators of B-ALL survival supporting new immune-based therapeutic approaches for high-risk B-ALL treatment.

Accuracy of Adverse Event Ascertainment in Clinical Trials for Pediatric Acute Myeloid Leukemia.

PURPOSE: Reporting of adverse events (AEs) in clinical trials is critical to understanding treatment safety, but data on AE accuracy are limited. This study sought to determine the accuracy of AE reporting for pediatric acute myeloid leukemia clinical trials and to test whether an external electronic data source can improve reporting. METHODS: Reported AEs were evaluated on two trials, Children's Oncology Group AAML03P1 and AAML0531 arm B, with identical chemotherapy regimens but with different toxicity reporting requirements. Chart review for 12 AEs for patients enrolled in AAML0531 at 14 hospitals was the gold standard. The sensitivity and positive predictive values (PPV) of the AAML0531 AE report and AEs detected by review of Pediatric Health Information System (PHIS) billing and microbiology data were compared with chart data. RESULTS: Select AE rates from AAML03P1 and AAML0531 arm B differed significantly and correlated with the targeted toxicities of each trial. Chart abstraction was performed on 204 patients (758 courses) on AAML0531. AE report sensitivity was < 50% for eight AEs, but PPV was > 75% for six AEs. AE reports for viridans group streptococcal bacteremia, a targeted toxicity on AAML0531, had a sensitivity of 78.3% and PPV of 98.1%. PHIS billing data had higher sensitivity (> 50% for nine AEs), but lower PPV (< 75% for 10 AEs). Viridans group streptococcal detection using PHIS microbiology data had high sensitivity (92.3%) and PPV (97.3%). CONCLUSION: The current system of AE reporting for cooperative oncology group clinical trials in pediatric acute myeloid leukemia underestimates AE rates. The high sensitivity and PPV of PHIS microbiology data suggest that using external data sources may improve the accuracy of AE reporting.

A comparison of discharge strategies after chemotherapy completion in pediatric patients with acute myeloid leukemia: a report from the Children's Oncology Group.

While most children receive acute myeloid leukemia (AML) chemotherapy as inpatients, there is variability in timing of discharge after chemotherapy completion. This study compared treatment-related morbidity, mortality and cumulative hospitalization in children with AML who were discharged after chemotherapy completion (early discharge) and those who remained hospitalized. Chart abstraction data for 153 early discharge-eligible patients enrolled on a Children's Oncology Group trial were compared by discharge strategy. Targeted toxicities included viridans group streptococcal (VGS) bacteremia, hypoxia and hypotension. Early discharge occurred in 11% of courses post-Induction I. Re-admission occurred in 80-100%, but median hospital stay was 7 days shorter. Patients discharged early had higher rates of VGS (adjusted risk ratio (aRR) = 1.67, 95% CI = 1.11-2.51), hypoxia (aRR = 1.92, 95% CI = 1.06-3.48) and hypotension (aRR = 4.36, 95% CI = 2.01-9.46), but there was no difference in mortality. As pressure increases to shorten hospitalizations, these results have important implications for determining discharge practices in pediatric AML.

Mitoxantrone as a substitute for daunorubicin during induction in newly diagnosed lymphoblastic leukemia and lymphoma.

BACKGROUND: Daunorubicin, a component of the four-drug induction chemotherapy regimen for de novo pediatric high-risk acute lymphoblastic leukemia (ALL) and lymphoblastic lymphoma (LLy), was unavailable in 2011 due to a national drug shortage. During this time, our institution substituted mitoxantrone 6.25 mg/m(2) for daunorubicin 25 mg/m(2) on induction Days 1, 8, 15, and 22. While mitoxantrone has been shown to be effective for relapsed ALL, it has not been studied in de novo pediatric ALL/LLy. PROCEDURE: We conducted a retrospective cohort study of newly diagnosed patients with ALL or LLy at our institution 1/2009-4/2013 to compare induction toxicity and response of patients treated with mitoxantrone versus daunorubicin. RESULTS: Eleven patients received mitoxantrone, 121 patients received daunorubicin. Induction toxicities including deaths, intensive care unit admissions, fever, bacteremia, and invasive fungal disease were similar for the two groups. Mean number of days hospitalized during induction was also similar (mitoxantrone 9.7 days vs. daunorubicin 11.2 days, P = 0.60). Minimal residual disease prevalence at the end of induction was not significantly different (mitoxantrone 33.3% vs. daunorubicin 23.0%, P = 0.44). The only significant difference between the groups was that a higher proportion of patients who received mitoxantrone had consolidation delayed due to myelosuppression (mitoxantrone 30.0% vs. daunorubicin 6.0%, P = 0.03). CONCLUSION: Induction toxicity and response for new ALL/LLy patients treated with mitoxantrone in place of daunorubicin were similar to the toxicity and response seen with conventional daunorubicin. Mitoxantrone is a reasonable replacement for daunorubicin in times of drug shortage.

Using improvement science to promote evidence-based practice in a childhood cancer and hematology center.

A major children's cancer and hematology center established a Quality Transformation (QT) Core to develop and monitor empirical outcomes that demonstrate excellence in clinical care. The QT Core, based on the Institute of Medicine's domains of quality health care, aims to ensure that care is safe, effective, patient centered, timely, efficient, and equitable. Specific goals for the first year of the QT Core were to develop a team of improvement science experts, engage faculty and staff in QT initiatives, promote accountability for excellence in clinical care, and establish specific metrics to evaluate process, structure, and outcomes for QT Core projects. The purpose of this article is to discuss the successful development of a quality transformation core within a pediatric subspecialty and demonstrate the principles of improvement science through an actual quality transformation project designed to implement an evidence-based guideline for procedural sedation for children with cancer. The QT Core within this subspecialty was founded on principles of successful transformation of patient care that includes motivation to change, leaders committed to quality, active engagement of staff in meaningful problem-solving initiatives, alignment with organization goals with resource allocation, and integration to bridge boundaries throughout an organization. These key principles are demonstrated through the discussion of the development of the QT Core and implementation of an evidence-based procedure sedation guideline. Pediatric and pediatric subspecialty groups can be on the forefront of national initiatives that promote quality health care, exemplified by the QT Core developed within the cancer and hematology center.

Meta-analysis of multiple microarray datasets reveals a common gene signature of metastasis in solid tumors.

BACKGROUND: Metastasis is the number one cause of cancer deaths. Expression microarrays have been widely used to study metastasis in various types of cancer. We hypothesize that a meta-analysis of publicly available gene expression datasets in various tumor types can identify a signature of metastasis that is common to multiple tumor types. This common signature of metastasis may help us to understand the shared steps in the metastatic process and identify useful biomarkers that could predict metastatic risk. METHODS: We identified 18 publicly available gene expression datasets in the Oncomine database comparing distant metastases to primary tumors in various solid tumors which met our eligibility criteria. We performed a meta-analysis using a modified permutation counting method in order to obtain a common gene signature of metastasis. We then validated this signature in independent datasets using gene set expression comparison analysis with the LS-statistic. RESULTS: A common metastatic signature of 79 genes was identified in the metastatic lesions compared with primaries with a False Discovery Proportion of less than 0.1. Interestingly, all the genes in the signature, except one, were significantly down-regulated, suggesting that overcoming metastatic suppression may be a key feature common to all metastatic tumors. Pathway analysis of the significant genes showed that the genes were involved in known metastasis-associated pathways, such as integrin signaling, calcium signaling, and VEGF signaling. To validate the signature, we used an additional six expression datasets that were not used in the discovery study. Our results showed that the signature was significantly enriched in four validation sets with p-values less than 0.05. CONCLUSIONS: We have modified a previously published meta-analysis method and identified a common metastatic signature by comparing primary tumors versus metastases in various tumor types. This approach, as well as the gene signature identified, provides important insights to the common metastatic process and a foundation for future discoveries that could have broad application, such as drug discovery, metastasis prediction, and mechanistic studies.