Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

The Boston University Photonics Center annual report 2016-2017

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2016-2017 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has undoubtedly been the Photonics Center’s best year since I became Director 10 years ago. In the following pages, you will see highlights of the Center’s activities in the past year, including more than 100 notable scholarly publications in the leading journals in our field, and the attraction of more than 22 million dollars in new research grants/contracts. Last year I had the honor to lead an international search for the first recipient of the Moustakas Endowed Professorship in Optics and Photonics, in collaboration with ECE Department Chair Clem Karl. This professorship honors the Center’s most impactful scholar and one of the Center’s founding visionaries, Professor Theodore Moustakas. We are delighted to haveawarded this professorship to Professor Ji-Xin Cheng, who joined our faculty this year.The past year also marked the launch of Boston University’s Neurophotonics Center, which will be allied closely with the Photonics Center. Leading that Center will be a distinguished new faculty member, Professor David Boas. David and I are together leading a new Neurophotonics NSF Research Traineeship Program that will provide $3M to promote graduate traineeships in this emerging new field. We had a busy summer hosting NSF Sites for Research Experiences for Undergraduates, Research Experiences for Teachers, and the BU Student Satellite Program. As a community, we emphasized the theme of “Optics of Cancer Imaging” at our annual symposium, hosted by Darren Roblyer. We entered a five-year second phase of NSF funding in our Industry/University Collaborative Research Center on Biophotonic Sensors and Systems, which has become the centerpiece of our translational biophotonics program. That I/UCRC continues to focus on advancing the health care and medical device industries

Support systems to guide clinical decision-making in precision oncology: The Cancer Core Europe Molecular Tumor Board Portal.

To the editor: the optimal management of cancer patients is increasingly dependent on individualized treatments guided by tumor sequencing data. As comprehensive genomic tests become routine in many disease settings and academic centers promote omics-guided clinical trial recruitment, accurate and scalable data interpretation represents a major challenge. The meticulous task of matching tumor alterations with approved or experimental therapies relies heavily on the expertise of each center

Community standards for open cell migration data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration

Development of a Deep learning-based pipeline to classify Small Round Cells Sarcomas histotypes

Ewing sarcoma (ES), Ewing-like sarcomas (ELS) and undifferentiated synovial sarcoma (SS) represent the main entities belonging to the family of the Small Round Cell Sarcomas (SRCS), a group of rare, heterogenous and highly aggressive mesenchymal tumors. SRCS are classified according to a specific single gene rearrangement. However, despite specific histological features are strongly correlated with the underlying molecular alteration, morphological overlapping may occur, and combined with their rarity, make the diagnosis challenging especially for non-expert pathologists. Within this context, the spreading of digital pathology and the recent developments of deep learning technologies for image processing, offer new opportunities for analysis, interpretation, and classification of histopathological slides. In this study, a deep learning-based framework called DeeRasNET, is specifically developed to classify hematoxylin and eosin-stained slides of ES, SS, BCOR and CIC rearranged sarcomas. Accuracy was the main metrics parameter used to evaluate the model performance. Initially, due to the small size of the datasets implemented for the model training, the classification accuracy for each class of sarcoma resulted low (mean accuracy of 0.6). To increase the performance of the model, we developed a pre-processing semi-automated pipeline comprising an open-source graphical interface unit (called TilerPath) with which we managed the tissue whole slide images, selecting interesting tissue areas and performing a quality control of the images used for classifier implementation. By TilerPath uninformative and misleading images were excluded from the model. After pre-preprocessing by Tilerpath, a total of 18193 tiles, selected from 124 digital slides covering all the four histotypes investigated, was used to train and test DeeRasNET. Finally, the scalability of the system was demonstrated on a validation dataset comprising 2706 tiles randomly selected from cases not included into the training and test set. After quality improvement, the final model showed a strong increase of classification performance, with accuracies ranging from 0.98 to 0.99 among all the sarcoma types. Both the TylerPath and the DeeRASnet source code were released as open-source software.Ewing sarcoma (ES), Ewing-like sarcomas (ELS) and undifferentiated synovial sarcoma (SS) represent the main entities belonging to the family of the Small Round Cell Sarcomas (SRCS), a group of rare, heterogenous and highly aggressive mesenchymal tumors. SRCS are classified according to a specific single gene rearrangement. However, despite specific histological features are strongly correlated with the underlying molecular alteration, morphological overlapping may occur, and combined with their rarity, make the diagnosis challenging especially for non-expert pathologists. Within this context, the spreading of digital pathology and the recent developments of deep learning technologies for image processing, offer new opportunities for analysis, interpretation, and classification of histopathological slides. In this study, a deep learning-based framework called DeeRasNET, is specifically developed to classify hematoxylin and eosin-stained slides of ES, SS, BCOR and CIC rearranged sarcomas. Accuracy was the main metrics parameter used to evaluate the model performance. Initially, due to the small size of the datasets implemented for the model training, the classification accuracy for each class of sarcoma resulted low (mean accuracy of 0.6). To increase the performance of the model, we developed a pre-processing semi-automated pipeline comprising an open-source graphical interface unit (called TilerPath) with which we managed the tissue whole slide images, selecting interesting tissue areas and performing a quality control of the images used for classifier implementation. By TilerPath uninformative and misleading images were excluded from the model. After pre-preprocessing by Tilerpath, a total of 18193 tiles, selected from 124 digital slides covering all the four histotypes investigated, was used to train and test DeeRasNET. Finally, the scalability of the system was demonstrated on a validation dataset comprising 2706 tiles randomly selected from cases not included into the training and test set. After quality improvement, the final model showed a strong increase of classification performance, with accuracies ranging from 0.98 to 0.99 among all the sarcoma types. Both the TylerPath and the DeeRASnet source code were released as open-source software

Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer

Objective Precision oncology depends on translating molecular data into therapy recommendations. However, with the growing complexity of next-generation sequencing-based tests, clinical interpretation of somatic genomic mutations has evolved into a formidable task. Here, we compared the performance of three commercial clinical decision support tools, that is, NAVIFY Mutation Profiler (NAVIFY; Roche), QIAGEN Clinical Insight (QCI) Interpret (QIAGEN) and CureMatch Bionov (CureMatch). Methods In order to obtain the current status of the respective tumour genome, we analysed cell-free DNA from patients with metastatic breast, colorectal or non-small cell lung cancer. We evaluated somatic copy number alterations and in parallel applied a 77-gene panel (AVENIO ctDNA Expanded Panel). We then assessed the concordance of tier classification approaches between NAVIFY and QCI and compared the strategies to determine actionability among all three platforms. Finally, we quantified the alignment of treatment suggestions across all decision tools. Results Each platform varied in its mode of variant classification and strategy for identifying druggable targets and clinical trials, which resulted in major discrepancies. Even the frequency of concordant actionable events for tier I-A or tier I-B classifications was only 4.3%, 9.5% and 28.4% when comparing NAVIFY with QCI, NAVIFY with CureMatch and CureMatch with QCI, respectively, and the obtained treatment recommendations differed drastically. Conclusions Treatment decisions based on molecular markers appear at present to be arbitrary and dependent on the chosen strategy. As a consequence, tumours with identical molecular profiles would be differently treated, which challenges the promising concepts of genome-informed medicine

Upfront Biology-Guided Therapy in Diffuse Intrinsic Pontine Glioma: Therapeutic, Molecular, and Biomarker Outcomes from PNOC003

PURPOSE PNOC003 is a multicenter precision medicine trial for children and young adults with newly diagnosed diffuse intrinsic pontine glioma (DIPG). PATIENTS AND METHODS Patients (3-25 years) were enrolled on the basis of imaging consistent with DIPG. Biopsy tissue was collected for whole-exome and mRNA sequencing. After radiotherapy (RT), patients were assigned up to four FDA-approved drugs based on molecular tumor board recommendations. H3K27M-mutant circulating tumor DNA (ctDNA) was longitudinally measured. Tumor tissue and matched primary cell lines were characterized using whole-genome sequencing and DNA methylation profiling. When applicable, results were verified in an independent cohort from the Children's Brain Tumor Network (CBTN). RESULTS Of 38 patients enrolled, 28 patients (median 6 years, 10 females) were reviewed by the molecular tumor board. Of those, 19 followed treatment recommendations. Median overall survival (OS) was 13.1 months [95% confidence interval (CI), 11.2-18.4] with no difference between patients who followed recommendations and those who did not. H3K27M-mutant ctDNA was detected at baseline in 60% of cases tested and associated with response to RT and survival. Eleven cell lines were established, showing 100% fidelity of key somatic driver gene alterations in the primary tumor. In H3K27-altered DIPGs, TP53 mutations were associated with worse OS (TP53mut 11.1 mo; 95% CI, 8.7-14; TP53wt 13.3 mo; 95% CI, 11.8-NA; P = 3.4e-2), genome instability (P = 3.1e-3), and RT resistance (P = 6.4e-4). The CBTN cohort confirmed an association between TP53 mutation status, genome instability, and clinical outcome. CONCLUSIONS Upfront treatment-naïve biopsy provides insight into clinically relevant molecular alterations and prognostic biomarkers for H3K27-altered DIPGs