Jyotishka Datta

Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL) is the most common form of blood cancer and is characterized by a striking degree of genetic and clinical heterogeneity. This heterogeneity poses a major barrier to understanding the genetic basis of the disease and its response to therapy. Here, we performed an integrative analysis of whole-exome sequencing and transcriptome sequencing in a cohort of 1,001 DLBCL patients to comprehensively define the landscape of 150 genetic drivers of the disease. We characterized the functional impact of these genes using an unbiased CRISPR screen of DLBCL cell lines to define oncogenes that promote cell growth. A prognostic model comprising these genetic alterations outperformed current established methods: cell of origin, the International Prognostic Index comprising clinical variables, and dual MYC and BCL2 expression. These results comprehensively define the genetic drivers and their functional roles in DLBCL to identify new therapeutic opportunities in the disease.

Asymptotic Properties of Bayes Risk for the Horseshoe Prior

In this paper, we establish some optimality properties of the multiple testing rule induced by the horseshoe estimator due to Carvalho, Polson, and Scott (2010, 2009) from a Bayesian decision theoretic viewpoint. We consider the two- groups model for the data and an additive loss structure such that the total loss is equal to the number of misclassified hypotheses. We use the same asymptotic framework as Bogdan, Chakrabarti, Frommlet, and Ghosh (2011) who introduced the Bayes oracle in the context of multiple testing and provided conditions under which the Benjamini-Hochberg and Bonferroni procedures attain the risk of the Bayes oracle. We prove a similar result for the horseshoe decision rule up to Op1q with the constant in the horseshoe risk close to the constant in the oracle. We use the Full Bayes estimate of the tuning parameter . It is worth noting that the Full Bayes estimate cannot be replaced by the Empirical Bayes estimate, which tends to be too small.

The genetic basis of hepatosplenic T-cell lymphoma

Hepatosplenic T-cell lymphoma (HSTL) is a rare and lethal lymphoma; the genetic drivers of this disease are unknown. Through whole-exome sequencing of 68 HSTLs, we define recurrently mutated driver genes and copy-number alterations in the disease. Chromatin-modifying genes, including SETD2, INO80, and ARID1B, were commonly mutated in HSTL, affecting 62% of cases. HSTLs manifest frequent mutations in STAT5B (31%), STAT3 (9%), and PIK3CD (9%), for which there currently exist potential targeted therapies. In addition, we noted less frequent events in EZH2, KRAS, and TP53SETD2 was the most frequently silenced gene in HSTL. We experimentally demonstrated that SETD2 acts as a tumor suppressor gene. In addition, we found that mutations in STAT5B and PIK3CD activate critical signaling pathways important to cell survival in HSTL. Our work thus defines the genetic landscape of HSTL and implicates gene mutations linked to HSTL pathogenesis and potential treatment targets.Significance: We report the first systematic application of whole-exome sequencing to define the genetic basis of HSTL, a rare but lethal disease. Our work defines SETD2 as a tumor suppressor gene in HSTL and implicates genes including INO80 and PIK3CD in the disease. Cancer Discov; 7(4); 369-79. ©2017 AACR.See related commentary by Yoshida and Weinstock, p. 352This article is highlighted in the In This Issue feature, p. 339.

Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2

Enteropathy-associated T cell lymphoma (EATL) is a lethal, and the most common, neoplastic complication of celiac disease. Here, we defined the genetic landscape of EATL through whole-exome sequencing of 69 EATL tumors. SETD2 was the most frequently silenced gene in EATL (32% of cases). The JAK-STAT pathway was the most frequently mutated pathway, with frequent mutations in STAT5B as well as JAK1, JAK3, STAT3, and SOCS1. We also identified mutations in KRAS, TP53, and TERT. Type I EATL and type II EATL (monomorphic epitheliotropic intestinal T cell lymphoma) had highly overlapping genetic alterations indicating shared mechanisms underlying their pathogenesis. We modeled the effects of SETD2 loss in vivo by developing a T cell–specific knockout mouse. These mice manifested an expansion of &ggr;&dgr; T cells, indicating novel roles for SETD2 in T cell development and lymphomagenesis. Our data render the most comprehensive genetic portrait yet of this uncommon but lethal disease and may inform future classification schemes.

GNA13 loss in germinal center B cells leads to impaired apoptosis and promotes lymphoma in vivo

GNA13 is the most frequently mutated gene in germinal center (GC)-derived B-cell lymphomas, including nearly a quarter of Burkitt lymphoma and GC-derived diffuse large B-cell lymphoma. These mutations occur in a pattern consistent with loss of function. We have modeled the GNA13-deficient state exclusively in GC B cells by crossing the Gna13 conditional knockout mouse strain with the GC-specific AID-Cre transgenic strain. AID-Cre(+) GNA13-deficient mice demonstrate disordered GC architecture and dark zone/light zone distribution in vivo, and demonstrate altered migration behavior, decreased levels of filamentous actin, and attenuated RhoA activity in vitro. We also found that GNA13-deficient mice have increased numbers of GC B cells that display impaired caspase-mediated cell death and increased frequency of somatic hypermutation in the immunoglobulin VH locus. Lastly, GNA13 deficiency, combined with conditional MYC transgene expression in mouse GC B cells, promotes lymphomagenesis. Thus, GNA13 loss is associated with GC B-cell persistence, in which impaired apoptosis and ongoing somatic hypermutation may lead to an increased risk of lymphoma development.

Some Remarks on Pseudo Panel Data

We discuss the possibility of constructing pseudo panel data from cross-sectional data, sampled at different points in time, by aligning individuals sharing some common characteristics into groups called “cohorts”. Based on a real-life example on income distribution in the USA, we construct and validate a pseudo panel data and compare this with real panel data. The agreement is encouraging.