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One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information.In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC).Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability.As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A.US National Institutes of Health.

Low-grade, low-stage endometrioid carcinomas (LGLS EC) demonstrate 5-yr survival rates up to 95%. However, a small subset of these tumors recur, and little is known about prognostic markers or established mutation profiles associated with recurrence. The goal of the current study was to identify the molecular profiles of the primary carcinomas and the genomic differences between primary tumors and subsequent recurrences. Four cases of LGLS EC with recurrence and 8 cases without recurrence were evaluated via whole-exome sequencing. Three of the 4 recurrent tumors were evaluated via Oncomine Comprehensive Assay. The resulting molecular profiles of the primary and recurrent tumors were compared. Two of the 3 recurrent cases showed additional mutations in the recurrence. One recurrent tumor included an additional TP53 mutation and the other recurrent tumor showed POLE and DDR2 kinase gene mutation. The POLE mutation occurred outside the exonuclease domain. PIK3CA mutations were detected in 4 of 4 primary LGLS EC with recurrence and in 3 of 8 disease-free cases. LGLS EC with recurrence showed higher MSIsensor scores compared with LGLS without recurrence. The level of copy number gains in LGLS EC with recurrence was larger than LGLS EC without recurrence. This pilot study showed 1 of 3 recurrent cases gained a mutation associated with genetic instability (TP53) and 1 of them also acquired a mutation in the DDR2 kinase, a potential therapeutic target. We also noted a higher level of copy number gains, MSIsensor scores and PIK3CA mutations in the primary tumors that later recurred.

Background A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. Purpose To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. Study type Retrospective. Population Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases). Field strength/sequence 1.5 to 3.0 Tesla, T2-weighted image pulse sequences. Assessment MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor. Statistical tests To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. Results Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high- vs. low-risk of prostate disease, respectively. Data conclusion AI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time. Level of evidence 1 TECHNICAL EFFICACY STAGE: 2.

Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. In addition, tumor purity has been shown to be correlated with survival outcomes for several diseases. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive. Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a slide, using multiple and different types of cancer. This approach allows for a flexible analysis of tumors from whole slide imaging (WSI) of histology hematoxylin and eosin (H&E) slides. Our model predicts tumor type with high accuracy (greater than 80% on an independent test cohort), and tumor purity at a higher accuracy compared to a comparable fully-supervised approach (0.1335 MAE on an independent test cohort). In addition to tumor purity prediction, our approach can identify high resolution tumor regions within a slide, to enrich tumor cell selection for downstream analyses. This model could also be used in a clinical setting, to stratify tumors into high and low tumor purity, using different thresholds, in a cancer-dependent manner, depending on what purity levels correlate with worse disease outcomes. In addition, this approach could be used in clinical practice to select the best tissue block for sequencing. Overall, this approach can be used in several different ways to analyze WSIs of tumor H&E sections.

Systematic implementation of bioinformatics resources for next generation sequencing (NGS)-based clinical testing is an arduous undertaking. One of the key challenges involves developing an ecosystem of information technology infrastructure for enabling scalable and reproducible bioinformatics services that is resilient and secure for handling genetic and protected health information, often embedded in an existing non-bioinformatics-oriented infrastructure. Container technology provides an ideal and infrastructure-agnostic solution for molecular laboratories developing and using bioinformatics pipelines, whether on-premise or using the cloud. A container is a technology that provides a consistent computational environment and enables reproducibility, scalability, and security when developing NGS bioinformatics analysis pipelines. Containers can increase the bioinformatics team's productivity by automating and simplifying the maintenance of complex bioinformatics resources, as well as facilitate validation, version control, and documentation necessary for clinical laboratory regulatory compliance. Although there is increasing popularity in adopting containers for developing NGS bioinformatics pipelines, there is wide variability and inconsistency in the usage of containers that may result in suboptimal performance and potentially compromise the security and privacy of protected health information. In this article, the authors highlight the current state and provide best or recommended practices for building, using containers in NGS bioinformatics solutions in a clinical setting with focus on scalability, optimization, maintainability, and data security.

Artificial intelligence (AI) systems have been proposed for reproductive medicine since 1997. Although AI is the main driver of emergent technologies in reproduction, such as robotics, Big Data, and internet of things, it will continue to be the engine for technological innovation for the foreseeable future. What does the future of AI research look like?

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

We call upon the research community to standardize efforts to use daily self-reported data about COVID-19 symptoms in the response to the pandemic and to form a collaborative consortium to maximize global gain while protecting participant privacy.

Information is the most potent protective weapon we have to combat a pandemic, at both the individual and global level. For individuals, information can help us make personal decisions and provide a sense of security. For the global community, information can inform policy decisions and offer critical insights into the epidemic of COVID-19 disease. Fully leveraging the power of information, however, requires large amounts of data and access to it. To achieve this, we are making steps to form an international consortium, Coronavirus Census Collective (CCC, coronaviruscensuscollective.org), that will serve as a hub for integrating information from multiple data sources that can be utilized to understand, monitor, predict, and combat global pandemics. These sources may include self-reported health status through surveys (including mobile apps), results of diagnostic laboratory tests, and other static and real-time geospatial data. This collective effort to track and share information will be invaluable in predicting hotspots of disease outbreak, identifying which factors control the rate of spreading, informing immediate policy decisions, evaluating the effectiveness of measures taken by health organizations on pandemic control, and providing critical insight on the etiology of COVID-19. It will also help individuals stay informed on this rapidly evolving situation and contribute to other global efforts to slow the spread of disease.In the past few weeks, several initiatives across the globe have surfaced to use daily self-reported symptoms as a means to track disease spread, predict outbreak locations, guide population measures and help in the allocation of healthcare resources. The aim of this paper is to put out a call to standardize these efforts and spark a collaborative effort to maximize the global gain while protecting participant privacy.

Magnetic Resonance Imaging (MRI) is routinely used to visualize the prostate gland and manage prostate cancer. The Prostate Imaging Reporting And Data System (PI-RADS) is used to evaluate the clinical risk associated with a potential tumor. However the PI-RADS score is subjective and its assessment varies between physicians. As a result, a definite diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis. A prostate biopsy is an invasive procedure and is associated with complications, including hematospermia, hematuria, and rectal bleeding.We hypothesized that an Artificial Intelligence (AI) can be trained on prostate cases where both imaging and biopsy are available to distinguish aggressive prostate cancer from non-aggressive lesions using MRI imaging only, that is, without the need for a biopsy.Our computational method, named AI-biopsy, can distinguish aggressive prostate cancer from non-aggressive disease with an AUC of 0.855 and a 79.02% accuracy. We used Class Activation Maps (CAM) to highlight which regions of MRI images are being used by our algorithm for classification, and found that AI-biopsy generally focuses on the same regions that trained uro-radiolosts focus on, with a few exceptions. In conclusion, AI-biopsy provides a data-driven and reproducible way to assess cancer aggressiveness from MRI images and a personalized strategy to reduce the number of unnecessary biopsies.

BACKGROUND Metastatic biopsies are increasingly being performed in patients with advanced prostate cancer to search for actionable targets and/or to identify emerging resistance mechanisms. Due to a predominance of bone metastases and their sclerotic nature, obtaining sufficient tissue for clinical and genomic studies is challenging. METHODS Patients with prostate cancer bone metastases were enrolled between February 2013 and March 2017 on an institutional review board-approved protocol for prospective image-guided bone biopsy. Bone biopsies and blood clots were collected fresh. Compact bone was subjected to formalin with a decalcifying agent for diagnosis; bone marrow and blood clots were frozen in optimum cutting temperature formulation for next-generation sequencing. Frozen slides were cut from optimum cutting temperature cryomolds and evaluated for tumor histology and purity. Tissue was macrodissected for DNA and RNA extraction, and whole-exome sequencing and RNA sequencing were performed. RESULTS Seventy bone biopsies from 64 patients were performed. Diagnostic material confirming prostate cancer was successful in 60 of 70 cases (85.7%). The median DNA/RNA yield was 25.5 ng/μL and 16.2 ng/μL, respectively. Whole-exome sequencing was performed successfully in 49 of 60 cases (81.7%), with additional RNA sequencing performed in 20 of 60 cases (33.3%). Recurrent alterations were as expected, including those involving the AR, PTEN, TP53, BRCA2, and SPOP genes. CONCLUSIONS This prostate cancer bone biopsy protocol ensures a valuable source for high-quality DNA and RNA for tumor sequencing and may be used to detect actionable alterations and resistance mechanisms in patients with bone metastases. Cancer 2017. © 2017 American Cancer Society.

PURPOSE We developed a precision medicine program for patients with advanced cancer using integrative whole-exome sequencing and transcriptome analysis. PATIENTS AND METHODS Five hundred fifteen patients with locally advanced/metastatic solid tumors were prospectively enrolled, and paired tumor/normal sequencing was performed. Seven hundred fifty-nine tumors from 515 patients were evaluated. RESULTS Most frequent tumor types were prostate (19.4%), brain (16.5%), bladder (15.4%), and kidney cancer (9.2%). Most frequently altered genes were TP53 (33%), CDKN2A (11%), APC (10%), KTM2D (8%), PTEN (8%), and BRCA2 (8%). Pathogenic germline alterations were present in 10.7% of patients, most frequently CHEK2 (1.9%), BRCA1 (1.5%), BRCA2 (1.5%), and MSH6 (1.4%). Novel gene fusions were identified, including a RBM47-CDK12 fusion in a metastatic prostate cancer sample. The rate of clinically relevant alterations was 39% by whole-exome sequencing, which was improved by 16% by adding RNA sequencing. In patients with more than one sequenced tumor sample (n = 146), 84.62% of actionable mutations were concordant. CONCLUSION Integrative analysis may uncover informative alterations for an advanced pan-cancer patient population. These alterations are consistent in spatially and temporally heterogeneous samples.

Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We implemented an AI approach based on deep neural networks (DNNs) to select highest quality embryos using a large collection of human embryo time-lapse images (about 50,000 images) from a high-volume fertility center in the United States. We developed a framework (STORK) based on Google’s Inception model. STORK predicts blastocyst quality with an AUC of >0.98 and generalizes well to images from other clinics outside the US and outperforms individual embryologists. Using clinical data for 2182 embryos, we created a decision tree to integrate embryo quality and patient age to identify scenarios associated with pregnancy likelihood. Our analysis shows that the chance of pregnancy based on individual embryos varies from 13.8% (age ≥41 and poor-quality) to 66.3% (age

Genomic testing with next-generation sequencing (NGS) has become a pillar of precision medicine, whose aim is to identify the genomic alterations of a patient’s tumor and provide guidelines to clinicians for optimal treatment. Clinical testing is typically performed with targeted panels interrogating a limited set of genes, selected based on our best scientific knowledge on their diagnostic or prognostic role. Despite more recent efforts to be more inclusive, most genomic databases have a limited representation of non-European populations, resulting in a biased selection of those genes, and the potential exclusion of under-represented groups from the benefit of precision medicine. At the Englander Institute for Precision Medicine (EIPM), we developed a whole-exome sequencing (WES) clinical test, EXaCT-1, which interrogates about 21,000 protein coding genes for single-nucleotide variants, indels, and copy number. EXaCT-1 enables an unbiased view of the genomic landscape of a patient’s tumor and allows for the collection of data to investigate genomic diversity. We also tackled one of the major barriers of precision medicine: the infrastructure to execute clinical sequencing. From ordering a test, collecting and processing samples, to the analysis and review of the data and generation of reports, several systems, procedures, and expertise are involved, and their effective coordination is a key component for the timely delivery of results. We have built a framework supporting the entire process of clinical genomic testing: a Laboratory Information Management System (LIMS) helps the clinical lab to receive orders, acquire and process specimens, and seamlessly communicate with the sequencers and the computational pipelines. Molecular pathologists use NGSReporter, a secure web application, to review the data and sign-out reports. NGSReporter integrates the results of a test with our Precision Medicine Knowledge Base (PMKB – https://pmkb.weill.cornell.edu), which classifies variants based on their relevance to clinical management and provides standardized interpretations. Reports are sent to the electronic health record (EHR) as PDFs as well as discrete entities, enabling queries such as: “Which Hispanic patients with KRAS mutations are diabetic?” Sharing de-identified data is also a key aspect of precision medicine. To this end, we provide our investigators and collaborators with a protected cBioPortal instance that, in addition to publicly available datasets, includes internal data, thus enabling the exploration of hypotheses about the role of alterations across different cohorts and clinical features. Being in the center of New York City has the added benefit of an ethnically diverse patient population. Finding the “right treatment for the right person and at the right time” requires a concerted effort of multiple partners. The EIPM infrastructure facilitates these efforts, with the goal of making precision medicine accessible to everyone. Citation Format: Andrea Sboner, Cora Sternberg, Juan Miguel Mosquera, Wei Song, Michael Kluk, Wayne Tam, Hanna Rennert, David Pisapia, Jeffrey Catalano, Gloria Cheang, David Wilkes, Danielle Bulaon, M. Laura Martin, Alexandros Sigaras, Kenneth Eng, Rohan Bareja, Rob Kim, Massimo Loda, Olivier Elemento. Precision medicine at Weill Cornell Medicine/New York Presbyterian: Breaking silos, integrating resources, being inclusive [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr IA33.

SummaryToward development of a precision medicine framework for metastatic, castration-resistant prostate cancer (mCRPC), we established a multi-institutional clinical sequencing infrastructure to conduct prospective whole-exome and transcriptome sequencing of bone or soft tissue tumor biopsies from a cohort of 150 mCRPC affected individuals. Aberrations of AR, ETS genes, TP53, and PTEN were frequent (40%–60% of cases), with TP53 and AR alterations enriched in mCRPC compared to primary prostate cancer. We identified new genomic alterations in PIK3CA/B, R-spondin, BRAF/RAF1, APC, β-catenin, and ZBTB16/PLZF. Moreover, aberrations of BRCA2, BRCA1, and ATM were observed at substantially higher frequencies (19.3% overall) compared to those in primary prostate cancers. 89% of affected individuals harbored a clinically actionable aberration, including 62.7% with aberrations in AR, 65% in other cancer-related genes, and 8% with actionable pathogenic germline alterations. This cohort study provides clinically actionable information that could impact treatment decisions for these affected individuals.