Performance of the McGill Interactive Pediatric OncoGenetic Guidelines for Identifying Cancer Predisposition Syndromes | Pediatric Cancer | JAMA Oncology | JAMA Network
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Figure 1.  Structure and Process of the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) eHealth Decision Support Tool
Structure and Process of the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) eHealth Decision Support Tool

Once a cancer type is selected, MIPOGG directs the user to a tumor-specific algorithm or a direct referral.

aOnce a referral for cancer predisposition syndrome (CPS) evaluation is recommended or not recommended, the MIPOGG tool offers the user access to tumor-specific educational modules that describe the known associations between each cancer type and cancer predisposition syndromes, along with hyperlinks to relevant published literature. Each cancer type has its own educational module.

bClose relative was considered a parent, sibling, aunt or uncle, grandparent, or first cousin.

cExcluding nonmelanoma skin cancer.

Figure 2.  Flowchart of the Study Processes Showing the Number of Patients Recommended or Not for Cancer Predisposition Syndrome (CPS) Evaluation by the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) and Number of Patients With Pediatric- or Adult-Onset CPSs
Flowchart of the Study Processes Showing the Number of Patients Recommended or Not for Cancer Predisposition Syndrome (CPS) Evaluation by the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) and Number of Patients With Pediatric- or Adult-Onset CPSs

HCPs indicates health care professionals.

Figure 3.  Cancer Predisposition Syndrome (CPS) Detection According to the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG)
Cancer Predisposition Syndrome (CPS) Detection According to the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG)

Most patients with no identified CPSs did not meet any MIPOGG criteria. Most patients with pediatric-onset CPSs were identified from direct referrals based on their tumor type, followed by those patients who met only tumor-specific criteria or who met some combination of both tumor-specific and universal criteria. Most patients with adult-onset CPSs met no MIPOGG criteria because MIPOGG is designed for recognizing patterns in pediatric-onset CPSs.

Table 1.  Demographic Characteristics of the Study Populationa
Demographic Characteristics of the Study Populationa
Table 2.  Test Characteristics of MIPOGG for Pediatric-Onset Cancer Predisposition Syndromes in Children With Cancer Who Underwent Comprehensive Genetic Testing (Study Phase 2)
Test Characteristics of MIPOGG for Pediatric-Onset Cancer Predisposition Syndromes in Children With Cancer Who Underwent Comprehensive Genetic Testing (Study Phase 2)
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    Original Investigation
    October 7, 2021

    Performance of the McGill Interactive Pediatric OncoGenetic Guidelines for Identifying Cancer Predisposition Syndromes

    Catherine Goudie, MD1,2; Leora Witkowski, PhD3; Noelle Cullinan, MD4,5; et al Lara Reichman, MSc2,3; Ian Schiller, MSc6; Melissa Tachdjian, BA2; Linlea Armstrong, MD7; Katherine A. Blood, MD, PhD7,8; Josée Brossard, MD9; Ledia Brunga, MD10; Chantel Cacciotti, MD11; Kimberly Caswell, BSc10; Sonia Cellot, MD, PhD12; Mary Egan Clark, BA13; Catherine Clinton, MS14; Hallie Coltin, MDCM, MSc5; Kathleen Felton, MD15; Conrad V. Fernandez, MD16; Adam J. Fleming, MD17; Noemi Fuentes-Bolanos, MD, PhD18,19; Paul Gibson, MD17; Ronald Grant, MD5; Rawan Hammad, MD5,20; Lynn W. Harrison, CCRP13; Meredith S. Irwin, MD5; Donna L. Johnston, MD21; Sarah Kane, MS22; Lucie Lafay-Cousin, MD, MSc23; Irene Lara-Corrales, MD24; Valerie Larouche, MD25; Natalie Mathews, MD5; M. Stephen Meyn, MD, PhD26,27; Orli Michaeli, MD5; Renée Perrier, MD28; Meghan Pike, MD16; Angela Punnett, MD5; Vijay Ramaswamy, MD, PhD5; Jemma Say, MBChB29; Gino Somers, MBBS, PhD30; Uri Tabori, MD5; My Linh Thibodeau, MD, MSc5,10; Annie-Kim Toupin, MD31,32; Katherine M. Tucker, MD33; Kalene van Engelen, MSc34; Stephanie Vairy, MD9,12; Nicolas Waespe, MD35,36; Meera Warby, MS33; Jonathan D. Wasserman, MD, PhD37,38; James A. Whitlock, MD5; Daniel Sinnett, PhD12; Nada Jabado, MD, PhD1,2; Paul C. Nathan, MD, MSc5; Adam Shlien, PhD10,38; Junne Kamihara, MD, PhD14; Rebecca J. Deyell, MD, MSc39; David S. Ziegler, MBBS, PhD18,19; Kim E. Nichols, MD13; Nandini Dendukuri, PhD6; David Malkin, MD5; Anita Villani, MD, MSc5; William D. Foulkes, MBBS, PhD40,41,42
    Author Affiliations
    • 1Division of Hematology-Oncology, Department of Pediatrics, McGill University Health Centre, Montreal, Quebec, Canada
    • 2Department of Child Health and Human Development, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
    • 3McGill University Health Centre, Department of Human Genetics, Montreal, Quebec, Canada
    • 4Department of Haematology-Oncology, Children’s Health Ireland, Crumlin, Dublin, Ireland
    • 5Division of Pediatric Hematology/Oncology, Department of Pediatrics, The Hospital for Sick Children, Toronto, Ontario, Canada
    • 6Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, McGill University, Montreal, Quebec, Canada
    • 7Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
    • 8Hereditary Cancer Program, BC Cancer, Vancouver, British Columbia, Canada
    • 9Division of Pediatric Hematology-Oncology, Department of Pediatrics, CIUSSS de l’Estrie – CHUS, Sherbrooke, Quebec, Canada
    • 10Department of Genetics and Genome Biology, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
    • 11Department of Pediatric Oncology-Hematology, Children’s Hospital–London Health Sciences Centre, London, Ontario, Canada
    • 12Charles-Bruneau Cancer Centre, Pediatric Hematology-Oncology Division, Centre Hospitalier Universitaire (CHU) Sainte-Justine Research Centre, Montreal, Quebec, Canada
    • 13Cancer Predisposition Division, Department of Oncology, St. Jude Children’s Research Hospital, Memphis, Tennessee
    • 14Department of Pediatric Oncology, Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts
    • 15Pediatric Hematology/Oncology, Jim Pattison Children’s Hospital, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
    • 16Division of Hematology/Oncology, Department of Pediatrics, IWK Health Centre, Halifax, Nova Scotia, Canada
    • 17Division of Pediatric Hematology/Oncology, McMaster Children’s Hospital, Hamilton, Ontario, Canada
    • 18Children’s Cancer Institute, Lowy Cancer Centre, University of New South Wales Sydney, Kensington, New South Wales, Australia
    • 19Kids Cancer Centre, Sydney Children’s Hospital, Randwick, New South Wales, Australia
    • 20Department of Haematology, King Abdulaziz University, Jeddah, Makkah, Saudi Arabia
    • 21Division of Hematology/Oncology, Children’s Hospital of Eastern Ontario, Ottawa, Ontario, Canada
    • 22Division of Clinical Genetics, Department of Hereditary Cancer and Genetics, Memorial Sloan-Kettering Cancer Center, Basking Ridge, New Jersey
    • 23Section of Pediatric Hematology Oncology and Bone Marrow Transplantation, Alberta Children’s Hospital, Calgary, Alberta, Canada
    • 24Section of Dermatology, Department of Pediatrics, The Hospital for Sick Children, Toronto, Ontario, Canada
    • 25Department of Pediatrics, Centre mère-enfant Soleil du CHU de Québec-Université Laval, Québec City, Quebec, Canada
    • 26Center for Human Genomics and Precision Medicine, University of Wisconsin School of Medicine and Public Health, Madison
    • 27Division of Clinical and Metabolic Genetics, Department of Pediatrics, and Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
    • 28Department of Medical Genetics, Alberta Children’s Hospital and Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
    • 29Paediatric Haematology/Oncology Programme, Bristol Children’s Hospital, Bristol, United Kingdom
    • 30Division of Pathology, Department of Pediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
    • 31Faculty of Medicine, Université Laval, Quebec, Canada
    • 32Northern Ontario School of Medicine Residency Program, Sudbury, Ontario, Canada
    • 33Hereditary Cancer Centre, Department of Oncology and Haematology, Prince of Wales Hospital, Randwick, New South Wales, Australia
    • 34Medical Genetics Program of Southwestern Ontario, London Health Sciences Centre, London, Ontario, Canada
    • 35CANSEARCH Research Platform in Pediatric Oncology and Hematology of the University of Geneva, Geneva, Switzerland
    • 36Childhood Cancer Research Group, Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
    • 37Division of Endocrinology, The Hospital for Sick Children, Toronto, Ontario, Canada
    • 38Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
    • 39Division of Pediatric Hematology/Oncology/BMT, University of British Columbia, British Columbia Children’s Hospital and Research Institute, Vancouver, British Columbia, Canada
    • 40Department of Medicine, McGill University, Montreal, Quebec, Canada
    • 41Department of Human Genetics, McGill University, Montreal, Quebec, Canada
    • 42Department of Oncology, McGill University, Montreal, Quebec, Canada
    JAMA Oncol. Published online October 7, 2021. doi:10.1001/jamaoncol.2021.4536
    Key Points

    Question  Can the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) differentiate between children with cancer who have a low or high likelihood of having a cancer predisposition syndrome?

    Findings  In phase 1 of this predictive accuracy study, MIPOGG facilitated earlier recognition of cancer predisposition syndromes in 412 pediatric oncology patients. In phase 2, MIPOGG demonstrated a favorable diagnostic accuracy profile for pediatric-onset cancer predisposition syndromes in 658 different pediatric oncology patients (no patient overlap between phases),with a positive predictive value of 17.6% and a negative predictive value of 98.6% when compared with germline DNA sequencing through precision medicine programs.

    Meaning  These results suggest that the MIPOGG electronic health tool may enhance pediatric oncology care by facilitating rapid cancer predisposition syndrome risk assessment and by standardizing the recommendations for cancer predisposition syndrome evaluation in an easy and accessible manner.

    Abstract

    Importance  Prompt recognition of a child with a cancer predisposition syndrome (CPS) has implications for cancer management, surveillance, genetic counseling, and cascade testing of relatives. Diagnosis of CPS requires practitioner expertise, access to genetic testing, and test result interpretation. This diagnostic process is not accessible in all institutions worldwide, leading to missed CPS diagnoses. Advances in electronic health technology can facilitate CPS risk assessment.

    Objective  To evaluate the diagnostic accuracy of a CPS prediction tool (McGill Interactive Pediatric OncoGenetic Guidelines [MIPOGG]) in identifying children with cancer who have a low or high likelihood of having a CPS.

    Design, Setting, and Participants  In this international, multicenter diagnostic accuracy study, 1071 pediatric (<19 years of age) oncology patients who had a confirmed CPS (12 oncology referral centers) or who underwent germline DNA sequencing through precision medicine programs (6 centers) from January 1, 2000, to July 31, 2020, were studied.

    Exposures  Exposures were MIPOGG application in patients with cancer and a confirmed CPS (diagnosed through routine clinical care; n = 413) in phase 1 and MIPOGG application in patients with cancer who underwent germline DNA sequencing (n = 658) in phase 2. Study phases did not overlap. Data analysts were blinded to genetic test results.

    Main Outcomes and Measures  The performance of MIPOGG in CPS recognition was compared with that of routine clinical care, including identifying a CPS earlier than practitioners. The tool’s test characteristics were calculated using next-generation germline DNA sequencing as the comparator.

    Results  In phase 1, a total of 413 patients with cancer (median age, 3.0 years; range, 0-18 years) and a confirmed CPS were identified. MIPOGG correctly recognized 410 of 412 patients (99.5%) as requiring referral for CPS evaluation at the time of primary cancer diagnosis. Nine patients diagnosed with a CPS by a practitioner after their second malignant tumor were detected by MIPOGG using information available at the time of the first cancer. In phase 2, of 658 children with cancer (median age, 6.6 years; range, 0-18.8 years) who underwent comprehensive germline DNA sequencing, 636 had sufficient information for MIPOGG application. When compared with germline DNA sequencing for CPS detection, the MIPOGG test characteristics for pediatric-onset CPSs were as follows: sensitivity, 90.7%; specificity, 60.5%; positive predictive value, 17.6%; and negative predictive value, 98.6%. Tumor DNA sequencing data confirmed the MIPOGG recommendation for CPS evaluation in 20 of 22 patients with established cancer-CPS associations.

    Conclusions and Relevance  In this diagnostic study, MIPOGG exhibited a favorable accuracy profile for CPS screening and reduced time to CPS recognition. These findings suggest that MIPOGG implementation could standardize and rationalize recommendations for CPS evaluation in children with cancer.

    Introduction

    Germline pathogenic and likely pathogenic variants (GPVs) in cancer predisposition genes are present in 7.6% to 14.0% of young patients with cancer1-5 and result in cancer predisposition syndromes (CPSs). The heterogeneous sequencing approaches, variability in the comprehensiveness of cancer gene panels used, and the diverse populations enrolled in these studies contribute to this range. Prompt CPS recognition has implications for individual patient care, cancer surveillance strategies, and consideration of genetic counseling and cascade testing for at-risk family members. Identification of CPSs relies on many factors, including recognition of phenotypic CPS features, tumor-specific and family history features, practitioner experience, and appropriately resourced health care settings. These factors combined contribute to wide variations in the likelihood that an individual child will be recognized as having a cancer secondary to an underlying CPS. Decision support tools for oncology patients based on phenotypic features and pretest probabilities can be useful to standardize practitioner recognition of appropriate patient referrals for CPS evaluation.6

    Some larger centers can identify CPSs by offering forms of genomic sequencing (tumor and/or germline DNA sequencing) to every oncology patient through research or clinical initiatives, potentially revealing an array of germline variants independent of pretest probability.7,8 This strategy circumvents the need to identify patients for CPS evaluation but is not the global standard of care. Most hospitals worldwide have limited access to dedicated cancer genetics services, and decision support tools can promote rational and judicious use of resources.

    The McGill Pediatric OncoGenetic Guidelines (MIPOGG) decision support tool, available in app and web-based formats, was developed after extensive review of CPS literature to assist health care professionals (HCPs) in identifying children with cancer who warrant genetic evaluation based on the likelihood of their cancer being a manifestation of an underlying CPS.9 Launched in 2019, MIPOGG contains more than 140 tumor-specific decisional algorithms that encompass all tumors listed in the International Childhood Cancer Classification and the 2016 World Health Organization Classification for Central Nervous System tumors, with embedded CPS educational modules. According to tumor type, an algorithm recommends an immediate direct referral (eTable 1 in the Supplement) or guides practitioners through sequential yes or no questions that comprise tumor-specific and universal criteria to generate a referral recommendation for or against CPS evaluation (Figure 1). A CPS evaluation is recommended if 1 or more tumor-specific or universal criteria are fulfilled. The structure and content of MIPOGG, previously published, are presented in the eMethods in the Supplement.9-11

    To evaluate the utility of MIPOGG in CPS prediction, we conducted a performance and diagnostic accuracy study in children with cancer using 2 complementary approaches. Phase 1 evaluated MIPOGG performance in identifying children with cancer and a known CPS (detected through routine clinical care); phase 2 quantified the MIPOGG test characteristics in children with cancer who underwent germline DNA sequencing through precision medicine programs.

    Methods

    In this international, multicenter diagnostic accuracy study, 1071 pediatric (<19 years of age) oncology patients who had a confirmed CPS (12 oncology referral centers) or who underwent germline DNA sequencing through precision medicine programs (6 centers) from January 1, 2000, to July 31, 2020, were studied. Exposures were MIPOGG application in patients with cancer and a confirmed CPS (diagnosed through routine clinical care; n = 413) in phase 1 and MIPOGG application in patients with cancer who underwent germline DNA sequencing (n = 658) in phase 2. Data analysts were blinded to genetic test results in phase 2. The performance of MIPOGG in CPS recognition was compared with that of routine clinical care, including identifying a CPS earlier than practitioners. The tool’s test characteristics were calculated using next-generation germline DNA sequencing as the comparator. The study was approved by the 2 main institutional review boards: the McGill University Health Centre Research Ethics Board and the SickKids Research Ethics Board. The institutional review boards deemed that informed consent was not required. All data were deidentified. Each collaborating site also obtained approval from their local institutional review boards. The study followed the Standards for Reporting of Diagnostic Accuracy (STARD) reporting guideline.

    Phase 1: MIPOGG Performance in Patients With Cancer and a Confirmed CPS
    Study Design and Participants

    After research ethics approvals, a multi-institutional collaboration that involved 12 Canadian pediatric oncology centers (eTable 2 in the Supplement) was established to evaluate MIPOGG performance in oncology patients (<19 years of age) who were investigated by their clinical team for and subsequently confirmed to have a CPS (cancer diagnosis from January 1, 2000, to December 31, 2018; CPS diagnosis at any time point). A confirmed CPS was defined as a molecular diagnosis (GPV in a known cancer predisposing gene) or a clinical diagnosis by a medical geneticist that fulfilled clinical criteria for a given syndrome.12-16 Patients were identified via query of institutional databases and by physician recollection.

    Data Collection

    Deidentified patient-level data that pertained to the primary cancer diagnosis and personal and family history (available at the time of primary cancer diagnosis) were retrospectively abstracted by institutional collaborators. Three authors (C.G., N.C., and L.R.) independently applied the appropriate MIPOGG tumor algorithm, noting the MIPOGG recommendation for each patient, with no interauthor disagreement. A successful application of MIPOGG is defined in the eMethods in the Supplement.

    Statistical Analysis

    We evaluated the proportion of patients with cancer and a confirmed CPS who were correctly identified for CPS evaluation by MIPOGG and the ability of MIPOGG to recommend CPS evaluation at an earlier time point than routine clinical care for patients whose CPS was diagnosed subsequent to a second cancer diagnosis.

    Phase 2: MIPOGG Accuracy in Patients With Cancer and Germline Sequencing Data
    Study Design and Participants

    After research ethics approvals, a multi-institutional collaboration that involved 6 institutions (3 in Canada, 2 in the US, and 1 in Australia) that offered comprehensive germline and tumor DNA sequencing to children (<19 years of age) with cancer (diagnosed from January 1, 2000, to July 31, 2020) was established. Enrollment indications (high-risk, relapsed, or refractory cancers [50.9%] or unselected patients with cancer [49.1%]) and genomic sequencing techniques performed are outlined in eTable 3 in the Supplement.

    Data Collection

    Deidentified patient-level data that pertained to the primary cancer diagnosis and personal and family history (available at the time of primary cancer diagnosis) were retrospectively abstracted by institutional collaborators. Three authors (C.G., L.R., and M.T.), blinded to sequencing results, independently applied the appropriate MIPOGG tumor algorithm, noting the MIPOGG recommendation for each case. Interauthor discrepancies were resolved by a fourth investigator (N.C.). The GPVs in CPS genes known to be associated with pediatric cancers were defined as pediatric-onset CPSs (eg, Li-Fraumeni syndrome and constitutional mismatch repair deficiency), whereas those known to predispose to adult-onset malignant tumors were deemed adult-onset CPSs (eg, heterozygous variants in BRCA1 [OMIM 113705] or ATM [OMIM 607585] or Lynch syndrome). Results of tumor sequencing were reviewed, and a second hit was defined as loss of heterozygosity (LOH) or a somatic pathogenic variant in the same gene as the GPV. Tumors were grouped into established or nonestablished associations to the identified CPS for a given patient based on expert consensus (eMethods in the Supplement). Carrier status was defined as a heterozygous state for a CPS gene that only predisposes individuals to cancer in a biallelic state. Carriers were not considered to have a CPS diagnosis. Variants in non–cancer-related genes were not captured.

    Statistical Analysis

    Sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and positive likelihood ratios (PLRs) and negative likelihood ratios (NLRs) of MIPOGG to identify pediatric-onset CPSs and adult-onset CPSs were calculated with 95% CIs using the Wilson score, stratified by cancer category (hematologic cancers, solid tumors, and central nervous system tumors) and subcategories. The 95% CI for the difference in 2 proportions using the Wilson score for 2 independent samples and the 95% CI for PLR and NLR based on the log method for the ratio of 2 proportions were calculated.17

    In both phases, we evaluated the contribution of direct referrals, universal, and tumor-specific criteria to CPS detection. Dichotomous data were calculated using frequencies and proportions, with 95% CIs. Continuous data were calculated using medians and IQRs.

    Results
    Phase 1: MIPOGG Performance in Patients With Cancer and a Confirmed CPS

    Across 12 Canadian sites, we identified 413 patients with cancer (median age, 3.0 years; range, 0-18 years) and a confirmed CPS, 412 of whom had sufficient information for MIPOGG application. Cancer categories and demographic data are provided in Table 1. Diagnoses of CPSs were confirmed molecularly (n = 306), clinically only (n = 102), or not specified (n = 5).

    MIPOGG correctly recommended 410 of 412 patients (99.5%) with a CPS for genetic evaluation at the time of primary cancer diagnosis (Figure 2A). Of 410 patients, 153 (37.3%) were identified by cancer type alone, whereas 257 (62.7%) were identified by 1 or more algorithmic criteria. MIPOGG did not trigger referral in 2 patients (0.5%): a child with rhabdomyosarcoma with a pathogenic TP53 variant (OMIM 191170) and another child with acute lymphoblastic leukemia with Weaver syndrome and subtle overgrowth features. Nine patients developed a second cancer before CPS diagnosis by HCPs; all would have been identified by MIPOGG using information available at primary cancer diagnosis (eTable 4 in the Supplement).

    Phase 2: MIPOGG Accuracy in Patients With Cancer and Germline Sequencing Data

    Of 658 children with cancer (median age, 6.6 years; range, 0-18.8 years) who underwent comprehensive germline DNA sequencing, 636 had sufficient information for MIPOGG application. Cancer categories and patient demographic characteristics are presented in Table 1. In 81 patients (12.3%), GPVs in CPS genes were identified, with a pediatric-onset CPS confirmed in 54 patients (8.2%) and an adult-onset CPS detected in 27 patients (4.1%).

    MIPOGG recommended CPS evaluation in 279 of 636 patients (43.9%), 49 (17.6%) of whom had a GPV in a pediatric-onset CPS gene. Conversely, MIPOGG did not recommend CPS evaluation in 357 of 636 patients (56.1%), 5 (1.4%) of whom had a GPV in a pediatric-onset CPS gene (difference, 16.2%; 95% CI, 11.8%-21.1%) (Figure 2B). The proportions of pediatric- and adult-onset CPSs detected using direct referrals and tumor-specific and/or universal criteria are shown in Figure 3. Genetic diagnoses, associated cancer types, and MIPOGG referral status are further described in eFigure 1 in the Supplement.

    The test characteristics of MIPOGG for pediatric-onset CPS were as follows: sensitivity, 90.7%; specificity, 60.5%; PPV, 17.6%; and NPV, 98.6% (Table 2). The PLR of 2.30 (95% CI, 2.01-2.62) and the NLR of 0.15 (95% CI, 0.07-0.35) indicate incremental value of both MIPOGG outcomes, with a higher informative value when CPS evaluation is not recommended. Differences between accuracy measurements for tumor subcategories are presented on a receiver operating characteristic plot (eFigure 2 in the Supplement) and were not statistically significant. Although not primarily designed to detect adult-onset CPSs, the test characteristics of MIPOGG in this group were as follows: sensitivity, 59.3%; specificity, 61.4%; PPV, 7.0%; and NPV, 96.9%.

    Although MIPOGG does not incorporate tumor sequencing data in its algorithms, we reviewed the presence of somatic second hits and LOH in the patients with GPVs in CPS genes. Fifty-five of 81 patients (67.9%) had available tumor DNA sequencing data (eTable 5 in the Supplement). Thirty of 55 (54.5%) had a second hit in the tumor, 22 of whom had an established pediatric cancer-CPS association. MIPOGG identified 20 of these 22 patients (90.9%) as requiring CPS evaluation (eTable 5 in the Supplement). The 2 patients who were not identified by MIPOGG had high-grade gliomas and a GPV in TP53 with somatic LOH of the wild-type allele (eTable 5 in the Supplement). Another patient with a central nervous system embryonal tumor and a monoallelic MSH2 (OMIM 609309) GPV, a cancer-CPS association that is not yet established, also had a second hit and was not recommended for CPS evaluation by MIPOGG (eTable 5 in the Supplement). A flowchart (eFigure 3 in the Supplement) details the presence or absence of a second hit for genes with and without an established association with a pediatric CPS as well as the MIPOGG recommendation for or against CPS evaluation.

    Discussion

    This diagnostic study assessed the accuracy and utility of the MIPOGG CPS screening tool in pediatric oncology. MIPOGG performed as well as HCPs in identifying patients at primary cancer diagnosis with a CPS and identified all patients who subsequently developed a second malignant tumor at an earlier time point than HCPs. Timely CPS recognition is critical to allow implementation of cancer surveillance and cascade testing, which can improve patient outcomes.18 MIPOGG exhibits a highly favorable CPS prediction profile in children with cancer who underwent comprehensive germline DNA sequencing, with an overall sensitivity of 90.7% and NPV of 98.6%. Because MIPOGG is a screening tool, a higher sensitivity at the expense of specificity was tolerated. The overall referral rate was 43.9%; among these children, 17.6% had a pediatric-onset CPS.

    In patients who underwent large-scale germline DNA sequencing (ranging from 48 genes to whole genome sequencing), MIPOGG missed 5 pediatric-onset CPS diagnoses; 2 patients had a somatic second hit in TP53 and fit established cancer-CPS associations, highlighting an area for refinement of the MIPOGG algorithms (high-grade glioma in this case). The remaining 3 patients did not exhibit LOH or fit established cancer-CPS associations and could be considered to have incidental (secondary) findings: the first patient had a low-grade glioma and a GPV in SDHA (OMIM 600857), the second had a Ewing sarcoma and a GPV in BAP1 (OMIM 603089), and the third patient had a Ewing sarcoma and a GPV in FH (OMIM 136850) (eTable 5 in the Supplement). The presence of a second hit in the 2 patients above with TP53 GPVs (eFigure 3 and eTable 5 in the Supplement) demonstrates where tumor sequencing data were most informative. Most other patients who had a second hit with an established cancer-CPS association (n = 20) were correctly identified by MIPOGG (eFigure 3 and eTable 5 in the Supplement). Therefore, tumor sequencing data were less helpful in these patients and would not have significantly improved the performance of MIPOGG.

    Strengths and Limitations

    This study has strengths, including a rigorous approach leveraging data pertaining to more than 1000 pediatric oncology patients with and without CPSs to validate MIPOGG as the only electronic health CPS screening tool in pediatric oncology that has completed a series of performance assessments establishing its diagnostic accuracy measures. Additional strengths include its high discriminative qualities and its external validation through assessment in heterogeneous pediatric cancer populations.19 Similar performance results were identified by Byrjalsen et al,8 who independently demonstrated that MIPOGG detected 85.7% of pediatric-onset CPSs in a nationwide pediatric oncology sequencing initiative (n = 198).

    This study also has limitations. In phase 1, most children identified by HCPs had phenotypic features, striking family histories, or tumor types suggestive of CPSs, creating a population bias and rendering patients more easily identifiable by MIPOGG. Nevertheless, these features replicate clinical scenarios, and we considered it essential to demonstrate that MIPOGG was not inferior to HCP practice. We also acknowledge limitations in the development of MIPOGG algorithms, which reflect known cancer-CPS associations at a specific time point and require continual updating.20 The incorporation of universal criteria, however, serves to potentially identify novel CPSs and previously unknown cancer-CPS associations. For example, the universal criteria identified a patient with a neuroblastoma and a pathogenic SMARCA4 variant (OMIM 603254), currently considered a secondary finding but one that may eventually be deemed a novel association.

    The disclosure of secondary findings in multigene panel sequencing in clinical and research settings continues to provoke debate, especially in pediatric populations.21,22 The current goal of MIPOGG is the identification of established pediatric cancer-CPS associations in a clinical context.

    Although more than 96% of patients had sufficient information to apply MIPOGG, both study phases involved retrospective data capture in which data accuracy is uncertain. Patients enrolled in phase 2 did not undergo identical DNA sequencing techniques, possibly contributing to an underestimation of MIPOGG performance in some cases. The MIPOGG referral rate of 43.9% leads to additional strain on cancer genetics services, arguing for new models of service delivery. In contrast, more than 50% of children were deemed at low risk for CPSs. Implementation of MIPOGG may therefore facilitate prioritization of genetic testing resources, with limited risk of missing CPS diagnoses (NLR, 0.15), which is particularly applicable in centers that lack the capacity to perform genetic testing in all patients or in situations where payer coverage issues need to be considered.

    Conclusions

    In this study, MIPOGG exhibited a highly favorable accuracy profile for CPS screening and is likely to enhance clinical oncology care by facilitating rapid CPS risk assessment and by standardizing approaches to CPS evaluation. MIPOGG will next be assessed prospectively in unselected children with cancer who are undergoing germline DNA sequencing. It will also be expanded to incorporate cancers in young adults, with cost-effectiveness evaluation. Finally, MIPOGG algorithms will be modified using artificial intelligence technologies that guide prioritization of possible CPSs through pattern recognition of phenotypes and genotypes in the setting of various tumors.

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    Article Information

    Accepted for Publication: June 29, 2021.

    Published Online: October 7, 2021. doi:10.1001/jamaoncol.2021.4536

    Corresponding Author: Catherine Goudie, MD, Department of Child Health and Human Development, Research Institute of the McGill University Health Centre, 5252 Maisonneuve Ouest, Room 3F.48, Montreal, Quebec, Canada, H4A 3S5 (catherine.goudie@mcgill.ca).

    Author Contributions: Dr Goudie had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Witkowski and Cullinan and Ms Reichman contributed equally to this work.

    Concept and design: Goudie, Cullinan, Brunga, Cacciotti, Coltin, Lara-Corrales, Tabori, Waespe, Whitlock, Jabado, Malkin, Villani, Foulkes.

    Acquisition, analysis, or interpretation of data: Goudie, Witkowski, Cullinan, Reichman, Schiller, Tachdjian, Armstrong, Blood, Brossard, Cacciotti, Caswell, Cellot, Clark, Clinton, Coltin, Felton, Fernandez, Fleming, Fuentes-Bolanos, Gibson, Grant, Hammad, Harrison, Irwin, Johnston, Kane, Lafay-Cousin, Larouche, Mathews, Meyn, Michaeli, Perrier, Pike, Punnett, Ramaswamy, Say, Somers, Tabori, Thibodeau, Toupin, Tucker, van Engelen, Vairy, Warby, Wasserman, Sinnett, Nathan, Shlien, Kamihara, Deyell, Ziegler, Nichols, Dendukuri, Malkin, Villani, Foulkes.

    Drafting of the manuscript: Goudie, Cullinan, Tachdjian, Brunga, Cacciotti, Caswell, Coltin, Harrison, Johnston, Warby, Dendukuri, Malkin, Foulkes.

    Critical revision of the manuscript for important intellectual content: Goudie, Witkowski, Cullinan, Reichman, Schiller, Armstrong, Blood, Brossard, Cacciotti, Cellot, Clark, Clinton, Coltin, Felton, Fernandez, Fleming, Fuentes-Bolanos, Gibson, Grant, Hammad, Irwin, Johnston, Kane, Lafay-Cousin, Lara-Corrales, Larouche, Mathews, Meyn, Michaeli, Perrier, Pike, Punnett, Ramaswamy, Say, Somers, Tabori, Thibodeau, Toupin, Tucker, van Engelen, Vairy, Waespe, Wasserman, Whitlock, Sinnett, Jabado, Nathan, Shlien, Kamihara, Deyell, Ziegler, Nichols, Malkin, Villani.

    Statistical analysis: Goudie, Cullinan, Reichman, Schiller, Michaeli, Dendukuri.

    Obtained funding: Goudie, Warby, Whitlock, Shlien.

    Administrative, technical, or material support: Goudie, Cullinan, Reichman, Tachdjian, Brunga, Cacciotti, Caswell, Cellot, Clark, Clinton, Coltin, Fuentes-Bolanos, Harrison, Irwin, Larouche, Michaeli, Ramaswamy, Somers, Thibodeau, Vairy, Warby, Wasserman, Sinnett, Jabado, Kamihara, Ziegler.

    Supervision: Goudie, Armstrong, Lara-Corrales, Punnett, Tabori, Jabado, Shlien, Deyell, Malkin, Villani, Foulkes.

    Conflict of Interest Disclosures: Dr Goudie reported that the idea of using an app as a decision support tool (McGill Interactive Pediatric OncoGenetic Guidelines [MIPOGG]) has been copyrighted by Drs Goudie, Foulkes, and Malkin. Ms Reichman reported receiving grant funding from the Canadian Institutes of Health Research (CIHR) during the conduct of the study and grant funding from TD Ready Challenge. Ms Tachdjian reported receiving grant funding from the CIHR during the conduct of the study. Dr Perrier reported receiving personal fees from AstraZeneca outside the submitted work. Dr Ramaswamy reported receiving personal fees from AstraZeneca outside the submitted work. Dr Waespe reported receiving grants from CANSEARCH Research Foundation outside the submitted work. Dr Wasserman reported receiving personal fees from Ipsen Pharmaceutical outside the submitted work. Dr Whitlock reported receiving grants from SickKids Foundation during the conduct of the study and nonfinancial support from Novartis clinical trials and advisory board fees from Amgen, Shire, and Jazz outside the submitted work. Dr Kamihara reported having a spouse with equity in Rome Therapeutics, TellBio Inc, and PanTher Therapeutics and who receives personal fees from Pfizer, NanoString Technologies, Foundation Medicine Inc, and EMD Millipore Sigma and grants from ACD-Biotechne, Puretech Health, and Ribon Therapeutics outside the submitted work. Dr Deyell reported receiving advisory board fees from Bayer Canada outside the submitted work. Dr Ziegler reported receiving grants from Medical Research Future Fund, Kids Cancer Alliance, Tour de Cure, Lions Club International Foundation, Australian Lions Children’s Cancer Research Foundation, The Kids Cancer Project, University of New South Wales, and Minderoo Foundation Collaborate Against Cancer Initiative during the conduct of the study and personal fees from Bayer, Amgen, Day One, and Novartis outside the submitted work. Dr Malkin reported receiving personal fees from Bayer Canada outside the submitted work. Dr Foulkes reported receiving grants from AstraZeneca (held on behalf of a third party) outside the submitted work. No other disclosures were reported.

    Funding/Support: Dr Goudie’s research is funded by project grant 407997 from the CIHR, operating grant 23445 from the Cancer Research Society, and grant 253761 from the Fonds de Recherche du Quebec–Santé. This research and the development of the MIPOGG platform were also made possible by the Cedars Cancer Foundation/Sarah’s Funds, the Montreal Children’s Hospital Foundation, the TD Ready Challenge, the Pediatric Oncology Group of Ontario (POGO Research Fellowship Award, 2017-2018), la Fondation Charles-Bruneau, the SickKids Foundation, the SickKids Garron Family Cancer Centre–Great Cycle Challenge, and the Cole Foundation. Dr Foulkes’ research is funded by the CIHR (FDN: 148390). Dr Malkin’s research is funded by the CIHR (FDN-143234).

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: Daniel Morgenstern, MD, PhD (Hospital for Sick Children, Toronto, Ontario, Canada), Glenn McCluggage, MD (Royal Group of Hospitals Trust, Belfast, Ireland), and Constantine A. Stratakis, MD (National Institutes of Health), provided expert review of some of the tumor-specific algorithms. Claudie Saulnier, BA (Digital Communication Strategist, Montreal, Quebec, Canada), and Bite Size Entertainment (software programming firm in Montreal) developed the MIPOGG platform. No compensation outside usual salaries were received by these individuals.

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