Maria Monica Gramatges

Background: There are limited real‐world comparative effectiveness data on different antibiotic prophylaxis regimens of children receiving treatment for acute myeloid leukemia (AML). Procedure: We compared the effectiveness of third/fourth‐generation cephalosporin (cephalosporin) or quinolone prophylaxis with no antibiotic prophylaxis to prevent bacteremia during frontline AML therapy using an established cohort from 17 institutions. The prophylaxis regimen was at the discretion of the treating clinician. Secondary outcomes were Clostridioides difficile infection (CDI), time to next chemotherapy course, and mortality. Results: A total of 550 pediatric patients with AML contributed data from 1758 chemotherapy courses. Incident bacteremia was lower following cephalosporin and quinolone prophylaxis compared to no prophylaxis in post‐induction I chemotherapy courses (6.9% cephalosporin vs. 33.9% no prophylaxis, risk ratio [RR] 0.20, p < 0.01; 20.8% quinolone vs. 33.9% no prophylaxis, RR 0.61, p < 0.01). Reduction in bacteremia risk was greater with cephalosporin than with quinolone prophylaxis (RR 0.33, p < 0.01). CDI was rare and was not increased in those receiving prophylaxis (2.5% quinolone vs. 3.6% cephalosporin vs. 5.7% no prophylaxis; p = 0.05). Time to next course of chemotherapy was similar between groups except during intensification I, where patients receiving cephalosporin prophylaxis had a longer time to next chemotherapy (50 days for cephalosporin vs. 41 days for no prophylaxis, p < 0.01). Deaths during treatment were rare. Conclusions: Cephalosporin and quinolone prophylaxis reduced bacteremia without an increased risk of CDI. Additional work is needed to assess the differential impact of prophylaxis regimen on subsequent use of empiric therapy and development of resistance.

Background: Hepatotoxicity is a common treatment‐related complication in pediatric acute lymphoblastic leukemia (ALL). Excess adiposity at diagnosis has been identified as a risk factor, but the impact of weight change during therapy has not been examined. Methods: We included patients aged 2–20 years newly diagnosed with ALL between 2005 and 2021. Hepatotoxicity was defined as: (1) transaminitis (ALT/AST > 10× the upper limit of normal), or (2) conjugated hyperbilirubinemia (cbili; > 3.0 mg/dL during induction/> 2.0 mg/dL post‐induction). Youden's J was used to identify optimal BMI z‐score thresholds. Cox proportional hazards regression was used to assess associations between hepatotoxicity during treatment and diagnostic BMI and post‐induction hepatotoxicity and weight change during induction. Associations were adjusted for age, race, ethnicity, and treatment risk stratification. Patients were censored at qualifying event or 1200 days post‐diagnosis. Results: Among 1070 patients (median age 6 years), 45.8% developed transaminitis and 7.6% cbili. Optimal BMI z‐score cut points were −1.5 and 1.5 for transaminitis and 1.5 for cbili. A BMI z‐score ≤ −1.5 was associated with reduced transaminitis (HR = 0.58, 95% CI: 0.39–0.86; p = 0.006), while a z‐score ≥ 1.5 was associated with increased transaminitis (HR = 1.32, 95% CI: 1.06–1.65; p = 0.01). A BMI z‐score ≥ 1.5 was also associated with increased cbili (HR = 1.97, 95% CI: 1.23–3.18; p = 0.01). Weight change was not associated with transaminitis, but weight loss during induction was associated with an increase in hazard of post‐induction cbili (HR = 1.08, 95% CI: 1.04–1.11; p < 0.0001). Conclusion: Diagnostic BMI z‐score and weight loss during induction were associated with increased hepatotoxicity risk, highlighting potential targets for improved risk stratification and hepatoprotective interventions.

PURPOSE: To address a lack of validated outcome measures of financial toxicity in pediatric oncology settings, we developed a novel caregiver-reported instrument. METHODS: We first conducted qualitative concept elicitation interviews with family caregivers, then drafted de novo survey items guided by salient content domains. Items were reviewed by an expert panel who provided numeric ratings of item relevance to calculate content validity index (CVI), along with feedback on clarity and content. Items were removed or revised through a consensus process, organized into a preliminary measure, and forward- and back-translated to Spanish. We pretested items with caregivers in English and Spanish through iterative rounds of language-concordant cognitive interviews, revising between rounds to optimize comprehension, decision/response processes, and flow. RESULTS: Concept elicitation with 21 caregivers (47% college-educated, 14% in Spanish) informed creation of 56 initial items across five content domains. CVI was <0.75 for 13 items; 12 were removed and one revised based on feedback. Of 43 items with CVI ≥0.75, eight were removed based on feedback and/or overlap with highly rated items. Thirty-six remaining items were revised and/or removed over six rounds of cognitive interviews with 22 different caregivers (42% college-educated, 23% in Spanish), then organized into a provisional 28-item instrument. In the final round of cognitive interviews, participants reported appropriate content, clarity, and organization in both languages. CONCLUSION: We developed a novel caregiver-reported instrument to assess financial toxicity in pediatric oncology settings, with content and face validity. Future quantitative testing will evaluate its psychometric properties and other dimensions of validity to facilitate practical use.

Background: Neurotoxicity is a rare, often dose‐limiting adverse effect of methotrexate (MTX) therapy that disproportionally affects Latino children. Factors contributing to the observed disparity are not well understood. This study leveraged admixture mapping to identify genetic regions associated with MTX‐related neurotoxicity susceptibility among Latino children undergoing therapy for pediatric acute lymphoblastic leukemia (ALL). Procedure: The study cohort was composed of 429 self‐identifying Latino children diagnosed with ALL (2005–2019) between the ages of 2 and 20 years treated at one of six US pediatric cancer treatment centers. MTX‐related neurotoxicity was defined as neurologic episodes (e.g., seizures, stroke‐like symptoms, altered mental status, and tremors) occurring within 21 days of intravenous (IV) and/or intrathecal (IT) MTX that were documented by magnetic resonance imaging (MRI) abnormalities and/or clinical symptoms. Among the 429 patients, we identified 46 (10.7%) Latino children with neurotoxicity. Admixture mapping was used to identify local genomic regions that are associated with the risk of MTX‐related neurotoxicity among Latino children with ALL. Conclusions: Ultimately, this study identified 11 genomic regions carrying suggestive associations with MTX‐neurotoxicity risk among Latino children with ALL. Review of biological plausibility of the loci within these regions revealed associations with areas of the brain or downstream regulatory functions. Results from this study may guide future work to further interrogate these regions through fine‐mapping to identify genetic variants associated with increased risk of MTX‐related neurotoxicity among Latino children with ALL.

Childhood cancer survivors have a heightened risk of developing subsequent neoplasms (SN) related to therapy. We analyzed whole-genome, exome, and RNA sequencing of 200 breast, meningioma, and thyroid SNs, which developed a median of 26.4 years after childhood cancer, among 160 survivors. Meningioma and thyroid SNs were enriched for driver gene rearrangements compared with de novo tumors, including NF2-disrupting alterations and kinase fusions potentially induced by radiation. Radiation correlated with increased insertion–deletion signature ID5. Nitrogen mustard treatment correlated with elevated “flat” signature SBS5 in breast and meningioma SNs; in vitro, these agents caused an unresolved flat signature associated with multiple flat signatures from the Catalogue of Somatic Mutations in Cancer. In meningioma, platinum therapy correlated with NF2 splice-site variants. Analysis of 19 multisample survivors revealed intrapatient heterogeneity in meningioma, including clonally independent tumors. These results demonstrate the long-term impact of childhood cancer treatment on the genomes of SNs developing in adulthood, which may guide SN treatment and prevention. Significance:This represents the most comprehensive genomic characterization of SNs from childhood cancer survivors to date, revealing the mutagenic impact of multiple therapies on the SN genome, including the potential impact of nitrogen mustards such as cyclophosphamide. These results may guide the optimization of future cancer treatment regimens to prevent SN development.See related commentary by Bertrums and van Boxtel, p. 1483

Supplementary Figure S1 shows SN sample exclusion, sequencing coverage, and sequencing types. Supplementary Figure S2 shows filtering approaches for exome FFPE artefacts in 33 pediatric tumors with matched FFPE vs. fresh-frozen data. Supplementary Figure S3 shows the correlation between SNV burden in SN exome vs. WGS data. Supplementary Figure S4 shows TCGA exome SNV and indel burden analysis for de novo cancer cohorts. Supplementary Figure S5 shows coverage data and absence of significant coverage effects on mutation burdens. Supplementary Figure S6 shows a comparison of SNV, indel, and SV burdens in 8 ovarian tumors with matched FFPE vs. fresh-frozen data. Supplementary Figure S7 shows de novo signature extraction data for SNV and indel signatures. Supplementary Figure S8 shows SNV signature data for thyroid SNs and dose-response analysis in meningioma SNs. Supplementary Figure S9 shows drug dose response curves in MCF10A cells. Supplementary Figure S10 shows the cisplatin signature in MCF10A cells. Supplementary Figure S11 shows additional details of experimental drug signatures and comparison signatures from other experimental studies. Supplementary Figure S12 shows the effects of simulated 4-OH-cyclophosphamide treatment on SNV signatures in patient tumors. Supplementary Figure S13 shows doublet signatures in SNs. Supplementary Figure S14 shows meningioma SN copy profiles. Supplementary Figure S15 shows details of functional NF2 splicing assays. Supplementary Figure S16 shows how probabilities were predicted for NF2 variant induction by platinum signatures. Supplementary Figure S17 shows breast SN copy profiles. Supplementary Figure S18 shows clonal evolution trees for breast, meningioma, and thyroid SNs. Supplementary Figure S19 shows details of clonal evolution in breast SNs. Supplementary Figure S20 shows details of clonal evolution in meningioma SNs. Supplementary Figure S21 shows details of clonal evolution in thyroid SNs. Supplementary Figure S22 shows selection of high-quality WGS samples for mutational signature analysis based on coverage breadth

Supplementary Table S1 shows SN sample clinical variables and metadata. Supplementary Table S2 shows a comparison of variables between good-quality vs. excluded samples. Supplementary Table S3 shows a summary of prior treatments across SN patients. Supplementary Table S4 shows a summary of original childhood cancer diagnoses for SN patients. Supplementary Table S5 shows metadata for 33 pediatric cancers with matched FFPE and fresh-frozen exome data. Supplementary Table S6 shows a list of coding-region somatic SNVs used for mutation burden analysis. Supplementary Table S7 shows multivariable analysis comparing SNV burdens between cohorts. Supplementary Table S8 shows multivariable analysis comparing age between cohorts. Supplementary Table S9 shows multivariable analysis comparing SNV burdens between thyroid cancer cohorts among samples with coverage below 100x. Supplementary Table S10 shows cancer-predisposing germline alterations among SN patients. Supplementary Table S11 shows a list of coding-region somatic indels used for mutation burden analysis. Supplementary Table S12 shows multivariable analysis comparing indel burdens between cohorts. Supplementary Table S13 shows SNV signature levels among SNs. Supplementary Table S14 shows multivariable analysis comparing SBS5 burdens in breast SNs stratified by prior treatment. Supplementary Table S15 shows multivariable analysis comparing SBS5 burdens in meningioma SNs stratified by prior treatment. Supplementary Table S16 shows multivariable analysis testing SBS5-cyclophosphamide dose-response relationship in meningioma SNs. Supplementary Table S17 shows variation in cyclophosphamide-containing regimens among meningioma SNs. Supplementary Table S18 shows indel signature levels among SNs. Supplementary Table S19 shows multivariable analysis testing ID5-radiation dose-response relationship in each SN type. Supplementary Table S20 shows multivariable analysis testing ID5-radiation dose-response relationship with all 3 SN types combined. Supplementary Table S21 shows NF2-disrupting structural variants detected in meningioma SNs. Supplementary Table S22 shows multivariable analysis comparing driver alteration frequency between meningioma cohorts. Supplementary Table S23 shows driver fusions in thyroid SNs. Supplementary Table S24 shows multivariable analysis comparing mutation group frequency between thyroid cancer cohorts. Supplementary Table S25 shows multivariable analysis comparing driver alteration frequency between thyroid cancer cohorts. Supplementary Table S26 shows multivariable analysis comparing driver alteration frequency between breast cancer cohorts

In 2018, the Texas Children's Cancer and Hematology Center Leukemia Program implemented a practice standard to support the transition from treatment to survivorship that includes shared, alternating care between leukemia and survivorship clinicians and a reminder to refer survivors to the long‐term survivor clinic (LTSC) 2 years after treatment. We evaluated the impact of this standard on LTSC attendance, early initiation, and follow‐up. Implementation was associated with early LTSC initiation, and early initiation was associated with subsequent LTSC follow‐up. Standardized referral processes and communication across care teams may improve survivorship care, although sociodemographic disparities in attendance and loss‐to‐follow‐up persist.

Childhood cancer survivors have a heightened risk of developing subsequent neoplasms (SN) related to therapy. We analyzed whole-genome, exome, and RNA sequencing of 200 breast, meningioma, and thyroid SNs, which developed a median of 26.4 years after childhood cancer, among 160 survivors. Meningioma and thyroid SNs were enriched for driver gene rearrangements compared with de novo tumors, including NF2-disrupting alterations and kinase fusions potentially induced by radiation. Radiation correlated with increased insertion–deletion signature ID5. Nitrogen mustard treatment correlated with elevated “flat” signature SBS5 in breast and meningioma SNs; in vitro, these agents caused an unresolved flat signature associated with multiple flat signatures from the Catalogue of Somatic Mutations in Cancer. In meningioma, platinum therapy correlated with NF2 splice-site variants. Analysis of 19 multisample survivors revealed intrapatient heterogeneity in meningioma, including clonally independent tumors. These results demonstrate the long-term impact of childhood cancer treatment on the genomes of SNs developing in adulthood, which may guide SN treatment and prevention. Significance: This represents the most comprehensive genomic characterization of SNs from childhood cancer survivors to date, revealing the mutagenic impact of multiple therapies on the SN genome, including the potential impact of nitrogen mustards such as cyclophosphamide. These results may guide the optimization of future cancer treatment regimens to prevent SN development.

Figure S1 shows a cross-validation plot to determine ‘k’ number of populations for the REDIAL dataset. Note the elbow of the scree plot at k = 4. This indicates that the genetic structure of the dataset is best explained by four ancestral populations, as adding more populations provides diminishing returns in reducing cross-validation error.