Biology subjects

Kulkarni, S.

Publications and source records attributed to Kulkarni, S..

5 recordsLinked to original sources

bioRxiv2018

Joint analysis of matched tumor samples with varying tumor contents improves somatic variant calling in the absence of a germline sample

Archival tumor samples represent a potential rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and germline variants that are private to the individual. Archival sections often contain adjacent normal tissue, but this normal tissue can include infiltrating tumor cells. Comparative somatic variant callers are designed to exclude variants present in the normal sample, so a novel approach is required to leverage sequencing of adjacent normal tissue for somatic variant calling. Here we present LumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient. The approach is based on the concept that the allelic fraction of somatic variants, but not germline variants, would be reduced in samples with low tumor content. LumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses the model to determine expected allelic fractions for somatic and germline variants and classify variants accordingly. To evaluate using LumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high tumor content, low tumor content, and germline exome sequencing data (to define true somatic variants) available for each patient. We show that both sensitivity and positive predictive value are improved by analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or compared to in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which normal samples were not available for germline sequencing, but tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using LumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent normal as a reference. Together, these results demonstrate the potential utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional DNA sample is not available.

bioinformatics↗

bioRxiv2018

Short-term effectiveness of HIV care coordination among persons with recent HIV diagnosis or history of poor HIV outcomes

The New York City HIV Care Coordination Program (CCP) combines multiple evidence-based strategies to support persons living with HIV (PLWH) at risk for, or with a recent history of, poor HIV outcomes. We assessed the comparative effectiveness of the CCP by merging programmatic data on CCP clients with population-based surveillance data on all New York City PLWH. A non-CCP comparison group of similar PLWH who met CCP eligibility criteria was identified using surveillance data. The CCP and non-CCP groups were matched on propensity for CCP enrollment within four baseline treatment status groups (newly diagnosed or previously diagnosed and either consistently unsuppressed, inconsistently suppressed or consistently suppressed). We compared CCP to non-CCP proportions with viral load suppression at 12-month follow-up. Among the 13,624 persons included, 15{middle dot}3% were newly diagnosed; among the 84{middle dot}7% previously diagnosed, 14{middle dot}2% were consistently suppressed, 28{middle dot}9% were inconsistently suppressed, and 41 {middle dot}6% were consistently unsuppressed in the year prior to baseline. At 12-month follow-up, 59{middle dot}9% of CCP and 53{middle dot}9% of non-CCP participants had viral load suppression (Relative Risk=1.11, 95%CI:1.08-1.14). Among those newly diagnosed and those consistently unsuppressed at baseline, the relative risk of viral load suppression in the CCP versus non-CCP participants was 1.15 (95%CI:1.09-1.23) and 1.32 (95%CI:1.23-1.42), respectively. CCP exposure shows benefits over no CCP exposure for persons newly diagnosed or consistently unsuppressed, but not for persons suppressed in the year prior to baseline. We recommend more targeted case finding for CCP enrollment and increased attention to viral load suppression maintenance.

epidemiology↗

bioRxiv2018

Inferring causal pathways among three or more variables from steady-state correlations in a homeostatic system

Cross-sectional correlations between two variables have limited implications for causality. We show here that in a homeostatic system with three or more inter-correlated variables, it is possible to make causal inferences from steady-state data. Every putative pathway between three variables makes a set of differential predictions that can be tested with steady state data. For example, among 3 variables, A, B and C, the coefficient of determination, [Formula] is predicted by the product of [Formula] and [Formula] for some pathways, but not for others. Residuals from a regression line are independent of residuals from another regression for some pathways, but positively or negatively correlated for certain other pathways. Different pathways therefore have different prediction signatures, which can be used to accept or reject plausible pathways. We apply these principles to test the classical pathway leading to a hyperinsulinemic normoglycemic insulin-resistant, or pre-diabetic state using four different sets of epidemiological data. Currently, a set of indices called HOMA-IR and HOMA-{beta} are used to represent insulin resistance and glucose-stimulated insulin response by {beta} cells respectively. Our analysis shows that if we assume the HOMA indices to be faithful indicators, the classical pathway must in turn, be rejected. Among the populations sampled, the classical pathway and faithfulness of the HOMA indices cannot be simultaneously true. The principles and tools described here can find wide application in inferring plausible regulatory mechanisms in homeostatic systems based on epidemiological data.

physiology↗

bioRxiv2017

Clingen Cancer Somatic Working Group: standardizing and democratizing access to cancer molecular diagnostic data to drive translational research

A growing number of academic and community clinics are conducting genomic testing to inform treatment decisions for cancer patients (1). In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing (2). The increasing availability and decreasing cost of tumor genomic profiling means that physicians can now make treatment decisions armed with patient-specific genetic information. Accumulating research in the cancer biology field indicates that there is significant potential to improve cancer patient outcomes by effectively leveraging this rich source of genomic data in treatment planning (3). To achieve truly personalized medicine in oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. One critical challenge to encoding variant data is adopting a standard of annotation of those variants that are clinically actionable. Through the NIH-funded Clinical Genome Resource (ClinGen) (4), in collaboration with NLMs ClinVar database and >50 academic and industry based cancer research organizations, we developed the Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations (5). We are currently involved in collaborative efforts to align the MVLD framework with parallel, complementary sequence variants interpretation clinical guidelines from the Association of Molecular Pathologists (AMP) for clinical labs (6). In order to truly democratize access to MolDx data for care and research needs, these standards must be harmonized to support sharing of clinical cancer variants. Here we describe the processes and methods developed within the ClinGens Somatic WG in collaboration with over 60 cancer care and research organizations as well as CLIA-certified, CAP-accredited clinical testing labs to develop standards for cancer variant interpretation and sharing.

bioinformatics↗

bioRxiv2017

RAPGEF5 regulates nuclear transport of β-catenin

Canonical Wnt signaling coordinates many critical aspects of embryonic development, while dysregulated Wnt signaling contributes to common diseases, including congenital malformations and cancer. The nuclear localization of {beta}-catenin is the defining step in pathway activation. However, despite intensive investigation, the mechanisms regulating {beta}-catenin nuclear transport remain undefined. In a patient with congenital heart disease and heterotaxy, a disorder of left-right patterning, we previously identified the guanine nucleotide exchange factor, RAPGEF5. Here, we demonstrate that RAPGEF5 regulates left-right patterning via Wnt signaling. In particular, RAPGEF5, regulates the nuclear translocation of {beta}-catenin independently of both {beta}-catenin cytoplasmic stabilization and the importin {beta}1/Ran mediated transport system. We propose a model whereby RAPGEF5 activates the nuclear GTPases, Rap1/2, to facilitate the nuclear transport of {beta}-catenin, defining a parallel nuclear transport pathway to Ran. Our results suggest new targets for modulating Wnt signaling in disease states.

developmental biology↗