The prognostic significance of anaplasia in childhood rhabdomyosarcoma: A report from the Children's Oncology Group

Shenoy, Archana; Alvarez, Elysia; Yun, Yueh; Li, Minjie; Shern, Jack F.; Khan, Javed; Hiniker, Susan M.; Granberg, Candace F.; Hawkins, Douglas S.; Parham, David M.; Teot, Lisa A.; Rudzinski, Erin R.

doi:10.1016/j.ejca.2020.10.018

Cited by 11 publications

(19 citation statements)

References 23 publications

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“… [ 35 ] 18 F (≤ 31) j KDM6A , c.1846dupA, p.Thr616Asnfs*5 NO Endometrial cancer NA NA ≤5% of endometrial cancer diagnosed in women ≤ 40 years NA [ 4 ] B-ALL B-cell acute lymphoblastic leukemia, BAP break-apart probe, BCL B-cell lymphoma, BM bone marrow, CD cluster of differentiation, CGH comparative genomic hybridization, CHARGE syndrome/MIM#214800 coloboma, heart defects, choanal atresia, retardation of growth/development, genital- and ear abnormalities, CNV copy number variation, COSMIC catalogue of somatic mutations in cancer, EBNA-2 Epstein-Barr nuclear-antigen 2, EBV Epstein-Barr virus, EMA epithelial membrane antigen, FISH fluorescence in situ hybridization, GFAP glial fibrillary acidic protein, GL germline, Het heterozygous, ISH in situ hybridization, Ki-67 Kiel clone 67, M months, Myf4 myogenin, MYOD1 myogenic differentiation 1, NA not analyzed/available Neg negative, NR not reported, OC Oral contraceptive, Pt patient, SMA smooth muscle actin, SOM reported as somatic variant in COSMIC database, TdT terminal deoxynucleotidyl transferase, T-ALL T-cell acute lymphoblastic leukemia, WHO World Health Organization, Yr year a Patients S1 with Burkitt lymphoma with insufficient clinical information regarding Kabuki syndrome is not present in this table but is included in Supporting Table S4 b Clinical diagnosis based on assessment of clinical features presented in the individual manuscripts by the author’s of the present study, see the “supplementary materials and methods” section c Diagnosis, clinico- and histopathologic features as provided in the original manuscript. These may not fulfil the present WHO-criteria for the respective tumors d References for common/general (clinical) presentation of individual tumor entities in Supporting data e Includes (other)potential predisposing (genetic) factors for the reported malignancy f Morphology does not show anaplastic features suggestive of a TP53 germline variant [ 143 , 144 ] g Initial tumor diagnosed as spindle cell hemangioma, 2nd tumor at same site as giant cell fibroblastoma h Diagnosed by long-distance polymerase chain reaction (PCR) i Karyotype at time of tumor diagnosis (peripheral blood) j Age at last examination …”

Section: Resultsmentioning

confidence: 99%

Molecular characterization of an embryonal rhabdomyosarcoma occurring in a patient with Kabuki syndrome: report and literature review in the light of tumor predisposition syndromes

et al. 2022

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Kabuki syndrome is a well-recognized syndrome characterized by facial dysmorphism and developmental delay/intellectual disability and in the majority of patients a germline variant in KMT2D is found. As somatic KMT2D variants can be found in 5–10% of tumors a tumor predisposition in Kabuki syndrome is discussed. So far less than 20 patients with Kabuki syndrome and a concomitant malignancy have been published. Here we report on a female patient with Kabuki syndrome and a c.2558_2559delCT germline variant in KMT2D who developed an embryonal rhabdomyosarcoma (ERMS) at 10 years. On tumor tissue we performed DNA-methylation profiling and exome sequencing (ES). Copy number analyses revealed aneuploidies typical for ERMS including (partial) gains of chromosomes 2, 3, 7, 8, 12, 15, and 20 and 3 focal deletions of chromosome 11p. DNA methylation profiling mapped the case to ERMS by a DNA methylation-based sarcoma classifier. Sequencing suggested gain of the wild-type KMT2D allele in the trisomy 12. Including our patient literature review identified 18 patients with Kabuki syndrome and a malignancy. Overall, the landscape of malignancies in patients with Kabuki syndrome was reminiscent of that of the pediatric population in general. Histopathological and molecular data were only infrequently reported and no report included next generation sequencing and/or DNA-methylation profiling. Although we found no strong arguments pointing towards KS as a tumor predisposition syndrome, based on the small numbers any relation cannot be fully excluded. Further planned studies including profiling of additional tumors and long term follow-up of KS-patients into adulthood could provide further insights.

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Section: Resultsmentioning

confidence: 99%

Molecular characterization of an embryonal rhabdomyosarcoma occurring in a patient with Kabuki syndrome: report and literature review in the light of tumor predisposition syndromes

et al. 2022

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“…Our CNNs accurately identified the most prognostically relevant genomic alterations in RMS ( PAX3/7–FOXO1 fusion and mutations in MYOD1 , TP53 ). TP53 mutations are present at diagnosis in 5% to 15% of patients with FN-RMS ( 14–16 ) and although anaplasia is associated with TP53 mutations in other tumors, only 60% to 70% of TP53 -mutant RMS display focal or diffuse anaplasia ( 32, 35 ). Moreover, only 24% of anaplastic RMS contains a TP53 mutation ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

“…TP53 mutations are present at diagnosis in 5% to 15% of patients with FN-RMS ( 14–16 ) and although anaplasia is associated with TP53 mutations in other tumors, only 60% to 70% of TP53 -mutant RMS display focal or diffuse anaplasia ( 32, 35 ). Moreover, only 24% of anaplastic RMS contains a TP53 mutation ( 32 ). Beyond histologic variability, intratumoral genetic heterogeneity may also be confounding as recently acquired TP53 mutations may be nonuniformly distributed within the tumor.…”

Section: Discussionmentioning

confidence: 99%

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Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group

Milewski

Jung

Brown

et al. 2022

Clinical Cancer Research

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Purpose: Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma which primarily occurs in children and young adults. We previously reported specific genomic alterations in RMS which strongly correlated with survival; however, predicting these mutations or high-risk disease at diagnosis remains a significant challenge. In this study, we utilized convolutional neural networks (CNNs) to learn histologic features associated with driver mutations and outcome using H&E images of RMS. Patients and Methods: Digital whole slide H&E images were collected from clinically annotated diagnostic tumor samples from n=321 RMS patients enrolled in Children’s Oncology Group (COG) trials (1998-2017). Patches were extracted and fed into deep learning CNNs to learn features associated with mutations and relative event-free survival risk. The performance of the trained models was evaluated against independent test sample data (n=136) or holdout test data. Results: The trained CNN could accurately classify alveolar RMS, a high-risk subtype associated with PAX3/7-FOXO1 fusion genes, with an ROC of 0.85 on an independent test dataset. CNN models trained on mutationally-annotated samples identified tumors with RAS pathway with a ROC of 0.67, and high-risk mutations in MYOD1 or TP53 with a ROC of 0.97 and 0.63, respectively. Remarkably, CNN models were superior in predicting event-free and overall survival compared to current molecular-clinical risk stratification. Conclusion: This study demonstrates that high-risk features, including those associated with certain mutations, can be readily identified at diagnosis using deep learning. CNNs are a powerful tool for diagnostic and prognostic prediction of rhabdomyosarcoma which will be tested in prospective COG clinical trials.

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“…Additionally, there are histologic features, beyond alveolar vs. embryonal, that have been assessed for their prognostic significance, in particular, the presence of anaplasia. Anaplasia is a histologic phenomenon that is associated with worse outcomes in other childhood cancers, such as Wilms tumor [125], and has been appreciated in cases of ERMS more commonly than in any other subtype of RMS [9]. Recent data suggest that anaplasia in RMS is not an independent adverse prognostic factor, with the caveat that larger studies may be necessary to confirm this finding [126].…”

Section: Risk Stratificationmentioning

confidence: 99%

Genetic Characterization, Current Model Systems and Prognostic Stratification in PAX Fusion-Negative vs. PAX Fusion-Positive Rhabdomyosarcoma

Dehner

Armstrong

Yohe

et al. 2021

Genes

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Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children and adolescents and accounts for approximately 2% of soft tissue sarcomas in adults. It is subcategorized into distinct subtypes based on histological features and fusion status (PAX-FOXO1/VGLL2/NCOA2). Despite advances in our understanding of the pathobiological and molecular landscape of RMS, the prognosis of these tumors has not significantly improved in recent years. Developing a better understanding of genetic abnormalities and risk stratification beyond the fusion status are crucial to developing better therapeutic strategies. Herein, we aim to highlight the genetic pathways/abnormalities involved, specifically in fusion-negative RMS, assess the currently available model systems to study RMS pathogenesis, and discuss available prognostic factors as well as their importance for risk stratification to achieve optimal therapeutic management.

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The prognostic significance of anaplasia in childhood rhabdomyosarcoma: A report from the Children's Oncology Group

Cited by 11 publications

References 23 publications

Molecular characterization of an embryonal rhabdomyosarcoma occurring in a patient with Kabuki syndrome: report and literature review in the light of tumor predisposition syndromes

Molecular characterization of an embryonal rhabdomyosarcoma occurring in a patient with Kabuki syndrome: report and literature review in the light of tumor predisposition syndromes

Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group

Genetic Characterization, Current Model Systems and Prognostic Stratification in PAX Fusion-Negative vs. PAX Fusion-Positive Rhabdomyosarcoma

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