SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events

BACKGROUND

Cancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational events.

RESULTS

Here, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix.

CONCLUSIONS

SigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions. SigProfilerMatrixGenerator is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/ .

Implementing Regulatory Broad Consent Under the Revised Common Rule: Clarifying Key Points and the Need for Evidence

The revised Common Rule includes a new option for the conduct of secondary research with identifiable data and biospecimens: regulatory broad consent. Motivated by concerns regarding autonomy and trust in the research enterprise, regulators had initially proposed broad consent in a manner that would have rendered it the exclusive approach to secondary research with all biospecimens, regardless of identifiability. Based on public comments from both researchers and patients concerned that this approach would hinder important medical advances, however, regulators decided to largely preserve the status quo approach to secondary research with biospecimens and data. The Final Rule therefore allows such research to proceed without specific informed consent in a number of circumstances, but it also offers regulatory broad consent as a new, optional pathway for secondary research with identifiable data and biospecimens. In this article, we describe the parameters of regulatory broad consent under the new rule, explain why researchers and research institutions are unlikely to utilize it, outline recommendations for regulatory broad consent issued by the Secretary's Advisory Committee on Human Research Protections (SACHRP), and sketch an empirical research agenda for the sorts of questions about regulatory broad consent that remain to be answered as the research community embarks on Final Rule implementation.

Methodology for characterizing emissions from small (0.5-2 MTD) batch-fed gasification systems using multiple waste compositions

A compact, containerized gasification system was characterized for air emissions while burning four waste types. A methodology is presented for developing a standardized test waste composition and demonstrated using three military and one civilian waste types. Batch charges of waste were processed through a gasification chamber, afterburner, and wet scrubber. The 0.5-2 metric ton per day (MTD) system was designed for mobile deployment by the military in forward operations but would be applicable to small scale civilian applications. Emissions data from these types of small capacity, cyclically operated systems are lacking, limiting efforts to compare technologies and their environmental performance. Eight tests were conducted in a 7-day period at the Kilauea Military Camp (KMC) in Hawaii. The pollutants characterized were chosen based on their regulatory and health relevance: particulate matter (PM), mercury (Hg), elemental composition, volatile organic compounds (VOCs), polyaromatic hydrocarbons (PAHs), and polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Averaged data from 4-hour runs, including startups and shutdowns, indicated that five of the nine EPA-regulated compounds (lead, cadmium, Hg, sulfur dioxide, and hydrogen chloride) were under the emission limits set for Other Solid Waste Incineration Units (OSWI) while four, PCDD/PCDF, PM, nitrogen oxides, and carbon monoxide, were higher. The procedures through which waste compositions were created and emissions were characterized provide a methodology by which differing waste to energy technologies can be compared on an equivalent basis. This system's emissions compare favorably with alternative disposal methods. PM and PCDD/PCDF emission factors were, respectively, over 39 and 9 times lower from this unit than from published data on burning simulated military waste in an air curtain incinerator and in open burn piles ("burn pits").

Abstract P5-07-01: Computational scoring of tumor infiltrating lymphocytes in triple-negative breast cancer

Background: Stromal Tumor Infiltrating Lymphocytes (sTIL) are an established prognostic feature in triple-negative breast cancer, yet manual assessment or visual estimation of sTILs with conventional light microscopy may be subject to inter-pathologist variability. Recently published guidelines by the International TIL Working Group help address inter-pathologist variability, yet there remains a need for more objective and quantitative computational sTIL scoring.

Methods: Our study used 120 triple-negative breast cancer slides (one slide per patient). A deep-learning based image analysis workflow is used to perform segmentation and classification of tissue regions and cells on the digital whole slide image. We used 14 annotated slides to train and validate the deep learning model, and to obtain image segmentation and classification accuracy statistics. Non-training slides were used to evaluate the concordance of manual (m-sTIL) and computationally derived (c-sTIL) scores. To generate data to create the model we manually annotated tissue regions in FFPE H&E stained digital slides, including: tumor, stroma, and necrosis. Initial classification of cell nuclei was performed using a semi-automated image analysis method, and then manually corrected to generate ground truth for tumor, stroma (fibroblasts), and lymphocytes. All annotations were performed by a trained research fellow and reviewed by a board-certified pathologist. Corresponding region and nucleus-level annotations were combined to train and validate a fully-convolutional neural network that jointly classifies tissue regions and cell nuclei. Tissue region segmentation accuracy was assessed by the Dice coefficient to measure degree of overlap between predicted tissue regions and ground truth annotations. Cell classification accuracy was assessed using area under curve (AUC). Two board-certified pathologists independently generated an m-sTIL score for all slides according to clinical guidelines, and discrepancies between pathologists were resolved by consensus. c-sTIL scores were calculated as the percentage of classified stromal areas occupied by nuclei classified as lymphocytic infiltrates.

Results: Tissue region segmentation was accurate for both stroma (0.77 Dice) and tumor (0.83 Dice) regions, and accurate overall (0.78 Dice). Cell classification was highly accurate for lymphocytes (0.89 AUC), tumor cells (0.90 AUC), stromal cells (0.78 AUC), and overall (0.89 AUC, micro average). Inter observer spearman correlation between the m-sTIL scores of our two pathologists was 0.66 (p < 0.001). By comparison, the correlation between c-sTIL and consensus m-sTIL was higher at 0.73 (p < 0.001). Dichotomizing at a threshold sTIL score of 10%, c-sTIL scoring identifies low-sTIL patients with an accuracy of 85%. High- and Low- sTIL score patient groups show clear separation on a Kaplan-Meier curve for both c-sTIL and m-sTIL scoring approaches.

Conclusions: Our pipeline quantifies stromal TILs with high concordance with manual pathologist scores, and sheds light on the ability of computational approaches in standardizing diagnostic pathology workflows. Future work will investigate how other computationally driven histology biomarkers can predict outcomes and help prognosticate breast cancer patients.

Citation Format: Amgad M, Sarkar A, Srinivas C, Redman R, Ratra S, Bechert CJ, Calhoun BC, Mrazeck K, Kurkure U, Cooper LA, Barnes M. Computational scoring of tumor infiltrating lymphocytes in triple-negative breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-07-01.

Physician Autonomy and the Opioid Crisis

The scope and severity of the opioid epidemic in the United States has prompted significant legislative intrusion into the patient-physician relationship. These proscriptive regulatory regimes mirror earlier legislation in other politically-charged domains like abortion and gun regulation. We draw on lessons from those contexts to argue that states should consider integrating their responses to the epidemic with existing medical regulatory structures, making physicians partners rather than adversaries in addressing this public health crisis.

The Case for Computational Health Science

In this introductory paper, we begin by making the case for Computational Health Science, which we define as the interdisciplinary efforts of health scientists, computer scientists, engineers, psychologists, and other social scientists, to conduct innovative research that will inform future practice directed at changing health behavior through improved communication, networking, and social capital. We recognize and discuss some of the main challenges involved with such an enterprise, but also highlight the associated benefits, which, we argue, significantly outweigh them. We then provide a brief summary of the contributions to this first Special Issue on Computational Health Science.

Revised 'Common Rule' Shapes Protections For Research Participants

Investigators and institutions have begun to prepare for new federal protections of study participants set to take effect in 2018.

Abstract P5-03-08: Development of a histology-based digital pathology image analysis algorithm for assessment of tumor infiltrating lymphocytes in HER2+ breast cancer

Introduction: Anthracycline-based chemotherapy regimens have been shown to increase risk of cardiac toxicity and other side effects especially in combination with HER2-targeting agents such as trastuzumab. Identification of biomarkers that can predict similar patient benefit in the context of targeted therapy between anthracycline and non-anthracycline-based regimens is attractive for personalized care. Histology-based assessment of tumor infiltrating lymphocytes (TILs) as a surrogate of the host immune response has been shown to be prognostic and potentially chemopredictive in triple-negative and HER2-positive breast cancers; however, the inter-play of TILs, tumor cells, other microenvironment mediators, their spatial relationships, quantity, and other image-based features have yet to be determined exhaustively and systemically. In anticipation of analyzing these aspects in the context of chemo and targeted therapy response in patient sample cohorts, we developed a digital pathology image analysis algorithm to identify tumor, stromal, and lymphocyte cells and acquire respective histology-based image features from hematoxylin and eosin (H&E) stained slides. Materials and Methods: An automated method involving cell detection, cell segmentation, feature extraction (capturing both local features and global context based features) and supervised machine learning (using a multi-class random forest based classifier, where a 3-class problem is represented using 3 1-vs-1 binary classifiers) were used to classify individual cells into the following 3 categories: tumor cells, stromal cells, and lymphocytes. Cell classification was compared against manually determined ground truth from three pathologists using simple confusion matrices. Results: From six H&E breast cancer cases, two pathologists manually and independently annotated the same tumor cells (6,458), lymphocytes (2,491), and stromal cells (744) in fourteen field-of-views (˜ 0.3 mm2 in size). Manual concordance of tumor cells (99.4%, 1434/1442), lymphocytes (80.0%, 680/849), and stromal cells (68.8%, 53/77) between two pathologists was moderate to high. Comparing only cells where two pathologists agreed (4,736) and an independent set of single cell annotations (547) from a third pathologist, image analysis classification showed high concordance for tumor cell (92.9%, 1107/1191), lymphocyte (90.4%, 572/636), and stromal cell (94.3%, 66/70)classification categories. Approximately 242 image features grouped into 22 unique data families were extracted from each cell analyzed. Conclusion: A H&E-derived TILs image analysis algorithm with associated feature extraction is feasible with preliminary findings of accurate cell classification. This tool will continue to be refined in anticipation of analysis in patient outcome cohorts.

Citation Format: Barnes M, Sarkar A, Redman R, Bechert C, Srinivas C. Development of a histology-based digital pathology image analysis algorithm for assessment of tumor infiltrating lymphocytes in HER2+ breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-03-08.

Source tracking using microbial community fingerprints: Method comparison with hydrodynamic modelling

Urban estuaries around the world are experiencing contamination from diffuse and point sources, which increases risks to public health. To mitigate and manage risks posed by elevated levels of contamination in urban waterways, it is critical to identify the primary water sources of contamination within catchments. Source tracking using microbial community fingerprints is one tool that can be used to identify sources. However, results derived from this approach have not yet been evaluated using independent datasets. As such, the key objectives of this investigation were: (1) to identify the major sources of water responsible for bacterial loadings within an urban estuary using microbial source tracking (MST) using microbial communities; and (2) to evaluate this method using a 3-dimensional hydrodynamic model. The Yarra River estuary, which flows through the city of Melbourne in South-East Australia was the focus of this study. We found that the water sources contributing to the bacterial community in the Yarra River estuary varied temporally depending on the estuary's hydrodynamic conditions. The water source apportionment determined using microbial community MST correlated to those determined using a 3-dimensional hydrodynamic model of the transport and mixing of a tracer in the estuary. While there were some discrepancies between the two methods, this investigation demonstrated that MST using bacterial community fingerprints can identify the primary water sources of microorganisms in an estuarine environment. As such, with further optimization and improvements, microbial community MST has the potential to become a powerful tool that could be practically applied in the mitigation of contaminated aquatic systems.

Clinical performance of the Solana® Point-of-Care Trichomonas Assay from clinician-collected vaginal swabs and urine specimens from symptomatic and asymptomatic women

BACKGROUND

Solana® (Quidel) is a new rapid (<40 min.) point-of-care (POC) test for qualitative detection of Trichomonas vaginalis (TV) DNA. The assay has two steps: 1) specimen preparation, and 2) amplification and detection using isothermal Helicase-Dependent Amplification (HDA). The objective was to demonstrate the performance of Solana for vaginal swabs and female urines based on comparison to wet mount and TV culture. Performance was also compared to the Aptima-TV assay.

METHODS

Urine and four clinician-collected vaginal swabs were collected. The first two were used for FDA composite reference (wet mount; InPouch TV Culture). The third swab was used for Solana. Sensitivity/specificity were based on the reference method. A specimen was considered positive if either test was positive. The fourth swab was for Aptima-TV.

RESULTS

Vaginal swabs and urines were obtained from 501 asymptomatic and 543 symptomatic women. Prevalence of TV by was 11.5%. For swabs, Solana® demonstrated high sensitivity and specificity from asymptomatic (100%/98.9%) and symptomatic (98.6%/98.5%) women, as well as for urines from asymptomatic (98.0%/98.4%) and symptomatic (92.9%/97.9%) women, compared to the reference method. Compared to Aptima-TV, the sensitivity/specificity was 89.7%/99.0% for swabs and 100%/98.9% for urines.

CONCLUSION

The Solana® assay performed well compared to the reference assays.

Responsibilities of Data Monitoring Committees: Consensus Recommendations

BACKGROUND

A data monitoring committee (DMC) has special responsibilities for protecting the safety of clinical trial participants. Few guidance documents are available that address the operations and mechanics of establishing, serving on, or reporting to a DMC. This article provides a practical guide to sponsors, institutions, and individuals responsible for, or serving on, a DMC.

METHODS

A workgroup of professionals from academia and not-for-profit and commercial organizations that included investigators, statisticians, patient advocates, and ethicists met to define the essential elements of planning, coordinating, and populating a DMC. All members of the group have formed, served on, advised, or worked with DMCs.

RESULTS

The group outlined the objectives and mechanics of running a DMC, including operational and practical considerations, membership characteristics, roles, members' liability, and indemnification. Further, it delineated the roles and responsibilities of each DMC member.

CONCLUSIONS

The group recommended practices for each phase of the DMC process from inception through execution of a clinical trial, with appropriate considerations for confidentiality. The group's practical guidance should assist in comprehensive oversight of appropriate clinical trials and should help DMC members execute their obligations with greater assurance.