Objective Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes. Materials and Methods We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent. Results The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence–driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases. Conclusions We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.
Background: As the repertoire of molecular targeted therapies for hematologic malignancies continues to expand, so too does the opportunity for molecular profiling to inform treatment decisions. While mutations in certain genes, such as JAK2, MPL, MYD88 and BRAF have diagnostic utility, others such as FLT3, NPM1, IDH1, IDH2, DNMT3A, KIT and CEBPA have prognostic value. Here, we present the development and clinical experience of MSK-IMPACT Heme (Integrated Mutation Profiling of Actionable Cancer Targets for Hematologic malignancies), a comprehensive molecular profiling platform, utilizing hybridization capture and high coverage next generation sequencing of paired tumor and normal tissues. Methods: We designed custom DNA probes corresponding to all exons of 400 key oncogenes and tumor suppressor genes implicated in hematologic malignancies, including all genes that are targetable by approved and experimental therapies being investigated in clinical trials at our institution. The accuracy, precision, and sensitivity of MSK-IMPACT Heme was assessed on a validation set of 113 unique tumor samples with known SNVs and indels previously confirmed by orthogonal methods. We implemented a custom analysis pipeline to integrate the analysis of any number of normal samples with a given tumor and provide a reliable assessment of somatic alterations, even in post-transplant chimeric patients. The selection of matched nail, saliva, and/or blood tissue was determined at the time of test initiation as indicated by patient diagnosis and transplant history. The ability to detect somatic copy number alterations was demonstrated with samples previously characterized by SNP array platforms. Results: We sequenced 821 tumor samples, from 759 patients that represented over 50 tumor types to a mean depth of 758X. 429 patients were male (56.5%) and 20 cases were post allogeneic stem cell transplantation. The most common tumor types sequenced were Follicular lymphoma (11.9%), DLBCL (11.3%), and AML (11.0%). We identified 4,935 mutations from 732 samples. The most commonly altered genes were TP53, KMT2D, and CREBBP. Implementation of the MSK-IMPACT Heme workflow enabled the characterization of complex tumor specimens, including sorted cells and tumor samples from post-transplant chimeric patients. The joint utilization of matched patient and donor normal tissues enabled differentiation between somatic alterations and both host and donor derived common polymorphisms. Conclusions: The MSK-IMPACT Heme assay provides molecular profiling of hematologic malignancies with high accuracy and sensitivity. Paired analysis of tumors and patient and/or donor matched normal tissue samples enables the unambiguous detection of somatic alterations and the ability apply these data towards tumor classification, risk assessment, prognosis, disease monitoring, and treatment optimization. Citation Format: Ryan N. Ptashkin, Ryma Benayed, John Ziegler, Anoop Balakrishnan Rema, Justyna Sadowska, Iwona Kiecka, Caleb Ho, JinJuan Yao, Christine Moung, Kseniya Petrova-Drus, Khedoudja Nafa, Connie Batlevi, Martin Tallman, Ross Levine, Sergio Giralt, Anas Younes, Marc Ladanyi, Mike Berger, Ahmet Zehir, Maria E. Arcila. MSK-IMPACT Heme: Validation and clinical experience of a comprehensive molecular profiling platform for hematologic malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3409.
Microsatellite instability (MSI) is a critical phenotype of cancer genomes and an FDA-recognized biomarker that can guide treatment with immune checkpoint inhibitors. Recent work has demonstrated that next-generation sequencing data can be used to identify samples with MSI-high phenotype. However, low tumor purity, as frequently observed in routine clinical samples, poses a challenge to the sensitivity of existing algorithms. To overcome this critical issue, we developed MiMSI, an MSI classifier based on deep neural networks and trained using a dataset that included low tumor purity MSI cases in a multiple instance learning framework. On a challenging yet representative set of cases, MiMSI showed higher sensitivity (0.940) and auROC (0.988) than MSISensor(sensitivity: 0.57; auROC: 0.911), an open-source software previously validated for clinical use at our institution using MSK-IMPACT large panel targeted NGS data.
Lynch syndrome (LS), an inherited predisposition syndrome associated with an increased risk of colorectal, endometrial, and other cancers, is characterized by germline mutations in mismatch repair pathway genes, which typically lead to microsatellite instability (MSI) in the resulting tumors. The FDA approval of pembrolizumab for all advanced MSI-H solid tumors has led to increasing MSI assessment. The presence of MSI in LS-associated tumors provides a unique and transformative opportunity for early detection and disease monitoring in these patients. Here we describe an approach to detect MSI from plasma cfDNA using MSK-ACCESS, a custom capture “liquid biopsy” approved for clinical use by the NY State Department of Health. In addition to frequently mutated exons of 129 genes, MSK-ACCESS also includes 165 highly informative microsatellite loci, selected from over 1,000 microsatellite regions based on >25,000 tumors sequenced using MSK-IMPACT, an FDA-authorized tumor sequencing panel. A key challenge in detecting MSI from cfDNA is the lack of ground truth in these samples, as cfDNA obtained from patients with MSI-high tumors may not always exhibit sufficient tumor-derived DNA fragments. To address this, we developed a machine learning approach for cfDNA analysis trained on orthogonally validated tumors sequenced via MSK-IMPACT. We present Allelic Distance-based Microsatellite Instability Estimator (ADMIE), an approach to translate deviation in tumor/cfDNA from normal/buffy coat DNA at individual microsatellite loci to a binary MSI call. ADMIE achieved a cross-validation precision of 1.00 +/- 0.02 and recall of 0.99 +/- 0.07. We ran this on 44 plasma samples collected from over 30 patients with MSI tumors including colorectal, prostate, and gastric cancers across multiple time points. We also evaluated plasma from 70 patients with known MSS tumors and 46 healthy controls. None of the cfDNA from healthy controls or patients with MSS tumors were found to be MSI positive, indicating high specificity. To establish our limit of detection, we performed in silico dilution experiments leveraging patient samples and MSI signal of biologic origin to simulate different tumor fractions, establishing our limit of detection at 1%. Among patients with MSI-high tumors, we found the presence and magnitude of MSI in the cfDNA to be correlated with measurable response to treatment with immunotherapy. In these patients, we detected MSI in the cfDNA of 6/8 samples where at least one mutation was detectable in plasma above 0.2% at baseline. Among the 4/6 patients for whom we had additional time points post treatment, we did not detect any mutations or evidence of MSI. In one patient, MSK-ACCESS indicated the presence of a second primary tumor based on the detection of MSI and mutations in cfDNA completely independent from those identified in the previously sequenced tumor. Our results suggest that MSI can be reliably detected in cfDNA using MSK-ACCESS and the MSI signature can represent a marker of occult metastatic disease in LS. This abstract is also being presented as Poster A54. Citation Format: Preethi Srinivasan, Alicia Latham, Zalak Patel, John Ziegler, Maysun Hasan, Juber A. Patel, Ian Johnson, Ronak Shah, Fanli Meng, Xiaohong Jing, Grittney Tam, Rose Brannon, Andrea Cercek, Ahmet Zehir, Brian Houck-Loomis, Dana Tsui, Zsofia Stadler, Michael F. Berger. MSI detection in plasma cfDNA: MSI as a marker of disease burden [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr PR07.
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