Michael A. Arnold

PURPOSE: Detection of bone marrow disease has been a major component of rhabdomyosarcoma (RMS) staging with bilateral bone marrow aspirates/biopsies (BMAB) from the iliac crests considered the diagnostic gold standard. This study's goal was to determine if 2- ¹⁸ F-labeled fluorodeoxyglucose positron emission tomography/computed tomography ([ ¹⁸ F]FDG PET/CT) or PET/magnetic resonance imaging (MRI) may replace BMAB in RMS staging. METHODS: Patients were recruited in Europe and North America. Medical records, FDG PET/CT or FDG PET/MRI (PET), and BMAB reports were collected retrospectively. Images were re-reviewed by nuclear medicine physicians at the patients' home institutions if PET reports indicated increased FDG uptake at bone/bone marrow sites or if tumor cells were detected in bone marrow samples. RESULTS: PET imaging and BMAB were performed before chemotherapy and tumor resection in 301 patients with FDG-avid RMS. Bone marrow metastases were detected by PET and BMAB in 54, by PET only in 24, and by BMAB only in one of 301 patients with FDG-avid primary tumors. Absence of bone marrow metastases was confirmed by PET and BMAB in 222 of 301 patients. These observations translated into 98% sensitivity and 90% specificity for PET in detecting bone marrow metastases when BMAB was considered as gold standard. FDG-avid marrow disease was patchy (1-5 foci) in one third of cases. Patients with marrow disease indicated by PET and BMAB had more FDG-avid bone marrow foci, more metastatic sites, and lower survival than those with marrow disease indicated by PET only, suggesting more advanced disease stages. CONCLUSION: Bone marrow sampling may be omitted in patients without evidence of FDG-avid bone marrow disease by PET. If there are FDG-avid bone marrow lesions, further investigation by MRI and/or biopsy should be considered.

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

Background: Survivors of childhood cancer face elevated risks for treatment-related late effects, including subsequent malignant neoplasms (SMN). In the general population, accelerated aging is associated with increased malignancy risk; however, this has not been evaluated in survivors. Methods: Survivors enrolled in the Childhood Cancer Survivor Study with measures of physiologic frailty, Cumulative Illness Rating Scale for Geriatrics (CIRS-G), or deficit accumulation index (DAI) data were included. Associations between these measures of accelerated aging with SMNs, benign meningiomas, and nonmelanoma skin cancers (NMSC) were estimated with piecewise exponential models, adjusted for sociodemographic, treatment, and modifiable factors. Results: Physiologic frailty and CIRS-G were not associated with risk of SMNs, meningiomas, or NMSCs. A higher DAI was only associated with increased risk of benign meningioma but not SMNs or NMSCs. Conclusions: Accelerated aging measures were not consistently associated with SMN risk in survivors of childhood cancer. Impact: Our results confirm that established risk factors, especially treatment exposures, remain critical in the development of SMNs in survivors, reinforcing the importance of risk-adapted therapies and long-term surveillance in survivorship care.

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.

Background: Survivors of childhood cancer face excess risk of developing basal cell carcinoma. Age-specific basal cell carcinoma risk prediction models for survivors may support targeted screening recommendations. Methods: We developed models predicting basal cell carcinoma risk by ages 40 and 50 years featuring detailed cancer treatment predictors, utilizing statistical and machine-learning algorithms and data from 23 166 five-year survivors in the Childhood Cancer Survivor Study, a multi-institutional retrospective cohort study. Selected models were externally validated in 5314 survivors in the St Jude Lifetime Cohort. Model discrimination and precision were evaluated using the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) and benchmarked against the current Children’s Oncology Group Long-Term Follow-Up Guidelines (COG LTFU, v6.0) for skin cancer screening. Results: By ages 40 and 50 years, basal cell carcinoma cumulative incidence was 5% and 15% in the Childhood Cancer Survivor Study and 7% and 21% in the St Jude Lifetime Cohort, respectively. The XGBoost algorithm-based models with treatment dose-specific predictors performed best, showing good external discrimination (age 40 years: AUROC = 0.75; age 50 years: AUROC = 0.76) and precision (age 40 years: AUPRC = 0.20; age 50 years: AUPRC = 0.52), outperforming COG LTFU Guideline-directed risk stratification (age 40 years: AUROC = 0.65; age 50 years: AUROC = 0.62; age 40 years: AUPRC = 0.09; age 50 years: AUPRC = 0.26; P < .01). These novel models reclassified 37% of survivors with COG-recommended skin cancer screening as low risk by age 40 years and 29% of survivors without COG-recommended screening as moderate or high risk by age 50 years, suggesting these recommendations overestimate risk in younger survivors and miss relevant predictors (eg, attained age, chemotherapy). Conclusions: In this study, we present validated basal cell carcinoma risk prediction models for childhood cancer survivors that outperform current practice guidelines. The associated online risk calculator can inform risk- and age-based screening recommendations.

Background: It is not known whether temporal changes in childhood cancer therapy have reduced risk of subsequent malignant neoplasms of the central nervous system (CNS), a frequently fatal late effect of cancer therapy. Methods: Five-year survivors of primary childhood cancers diagnosed between 1970 and 1999 in the Childhood Cancer Survivor Study with CNS subsequent malignant neoplasms were identified. Cumulative incidence rates and standardized incidence ratios were compared among survivors diagnosed between 1970-1979 (n = 6223), 1980-1989 (n = 9680), and 1990-1999 (n = 8999). Multivariable models assessed risk factors for CNS subsequent malignant neoplasms. Results: A total of 157 CNS subsequent malignant neoplasms (1970s, 52; 1980s, 63; 1990s, 42) were identified, excluding meningiomas, which were most often malignant gliomas. The proportion of survivors receiving any cranial radiotherapy exposure was reduced over time (1970s, 77.0%; 1980s, 54.3%; 1990s, 33.9%), while the proportion receiving more than 35 Gy cranial radiotherapy showed a smaller reduction (11.4%, 10.8%, and 8.5%, respectively). Twenty-year cumulative incidence and standardized incidence ratios for CNS subsequent malignant neoplasms by treatment decade were 0.32% (95% confidence interval = 0.18% to 0.46%) and 6.6 (95% CI = 5.0 to 8.7); 0.55% (95% CI = 0.41% to 0.70%) and 8.3 (95% CI = 6.6 to 10.4); and 0.43% (95% CI = 0.31% to 0.55%) and 9.2 (95% CI = 7.0 to 12.0), respectively, with no statistically significant decreases between eras. Multivariable analyses showed increased risk for cranial radiotherapy dose levels more than 10 Gy and for primary diagnoses of medulloblastoma and/or primitive neuro-ectodermal tumor (hazard ratio [HR] = 18.7, 95% CI = 9.2 to 37.9) and astrocytoma (HR = 10.1, 95% CI = 5.3 to 19.5). Three-year cumulative incidence of death after CNS subsequent malignant neoplasms, by treatment decade, were 76%, 74%, and 73%, respectively. Conclusion: CNS subsequent malignant neoplasm incidence has not decreased despite fewer survivors exposed to CNS-directed radiotherapy. CNS subsequent malignant neoplasm remains a substantial source of mortality for affected patients.