Ravi Pandya

Orleans: cloud computing for everyone

Cloud computing is a new computing paradigm, combining diverse client devices -- PCs, smartphones, sensors, single-function, and embedded -- with computation and data storage in the cloud. As with every advance in computing, programming is a fundamental challenge, as the cloud is a concurrent, distributed system running on unreliable hardware and networks. Orleans is a software framework for building reliable, scalable, and elastic cloud applications. Its programming model encourages the use of simple concurrency patterns that are easy to understand and employ correctly. It is based on distributed actor-like components called grains, which are isolated units of state and computation that communicate through asynchronous messages. Within a grain, promises are the mechanism for managing both asynchronous messages and local task-based concurrency. Isolated state and a constrained execution model allow Orleans to persist, migrate, replicate, and reconcile grain state. In addition, Orleans provides lightweight transactions that support a consistent view of state and provide a foundation for automatic error handling and failure recovery. We implemented several applications in Orleans, varying from a messaging-intensive social networking application to a data- and compute-intensive linear algebra computation. The programming model is a general one, as Orleans allows the communications to evolve dynamically at runtime. Orleans enables a developer to concentrate on application logic, while the Orleans runtime provides scalability, availability, and reliability.

Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid- and lymphoid-derived hematologic malignancies

Significance Mononuclear cells obtained from freshly isolated patient samples with various hematologic malignancies were evaluated for sensitivities to combinations of drugs that target specific cell-signaling pathways. The diagnostic, genetic/cytogenetic, and cellular features of the patient samples were correlated with effective drug combinations. For myeloid-derived tumors, such as acute myeloid leukemia, several combinations of targeted agents that include a kinase inhibitor and venetoclax, a selective inhibitor of BCL2, are effective. Translating the genetic and epigenetic heterogeneity underlying human cancers into therapeutic strategies is an ongoing challenge. Large-scale sequencing efforts have uncovered a spectrum of mutations in many hematologic malignancies, including acute myeloid leukemia (AML), suggesting that combinations of agents will be required to treat these diseases effectively. Combinatorial approaches will also be critical for combating the emergence of genetically heterogeneous subclones, rescue signals in the microenvironment, and tumor-intrinsic feedback pathways that all contribute to disease relapse. To identify novel and effective drug combinations, we performed ex vivo sensitivity profiling of 122 primary patient samples from a variety of hematologic malignancies against a panel of 48 drug combinations. The combinations were designed as drug pairs that target nonoverlapping biological pathways and comprise drugs from different classes, preferably with Food and Drug Administration approval. A combination ratio (CR) was derived for each drug pair, and CRs were evaluated with respect to diagnostic categories as well as against genetic, cytogenetic, and cellular phenotypes of specimens from the two largest disease categories: AML and chronic lymphocytic leukemia (CLL). Nearly all tested combinations involving a BCL2 inhibitor showed additional benefit in patients with myeloid malignancies, whereas select combinations involving PI3K, CSF1R, or bromodomain inhibitors showed preferential benefit in lymphoid malignancies. Expanded analyses of patients with AML and CLL revealed specific patterns of ex vivo drug combination efficacy that were associated with select genetic, cytogenetic, and phenotypic disease subsets, warranting further evaluation. These findings highlight the heuristic value of an integrated functional genomic approach to the identification of novel treatment strategies for hematologic malignancies.

Simplifying research access to genomics and health data with Library Cards.

The volume of genomics and health data is growing rapidly, driven by sequencing for both research and clinical use. However, under current practices, the data is fragmented into many distinct datasets, and researchers must go through a separate application process for each dataset. This is time-consuming both for the researchers and the data stewards, and it reduces the velocity of research and new discoveries that could improve human health. We propose to simplify this process, by introducing a standard Library Card that identifies and authenticates researchers across all participating datasets. Each researcher would only need to apply once to establish their bona fides as a qualified researcher, and could then use the Library Card to access a wide range of datasets that use a compatible data access policy and authentication protocol.

Registered access: authorizing data access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model—“registered access”—to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

Project Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data

Motivation Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers, and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data. Results Here we describe ‘Dhaka’, a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and 6 single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data. Availability and Implementation All the datasets used in the paper are publicly available and developed software package is available on Github https://github.com/MicrosoftGenomics/Dhaka. Supporting info and Software: https://github.com/MicrosoftGenomics/Dhaka