Citations

SNF'ing out antitumor immunity Immune checkpoint inhibitors induce durable tumor regressions in some, but not all, cancer patients. Understanding the mechanisms that determine tumor sensitivity to these drugs could potentially expand the number of patients who benefit (see the Perspective by Ghorani and Quezada). Pan et al. discovered that tumor cells in which a specific SWI/SNF chromatin remodeling complex had been experimentally inactivated were more sensitive to T cell–mediated killing. The cells were more responsive to interferon-γ, leading to increased secretion of cytokines that promote antitumor immunity. Miao et al. examined the genomic features of tumors from patients with metastatic renal cell carcinoma who had been treated with immune checkpoint inhibitors. Tumors harboring inactivating mutations in PBRM1 , which encodes a subunit of the same SWI/SNF complex, were more likely to respond to the drugs. Science , this issue p. 770 , p. 801 ; see also p. 745

AbstractMotivationOne objective of human microbiome studies is to identify differentially abundant microbes across biological conditions. Previous statistical methods focus on detecting the shift in the abundance and/or prevalence of the microbes and treat the dispersion (spread of the data) as a nuisance. These methods also assume that the dispersion is the same across conditions, an assumption which may not hold in presence of sample heterogeneity. Moreover, the widespread outliers in the microbiome sequencing data make existing parametric models not overly robust. Therefore, a robust and powerful method that allows covariate-dependent dispersion and addresses outliers is still needed for differential abundance analysis.ResultsWe introduce a novel test for differential distribution analysis of microbiome sequencing data by jointly testing the abundance, prevalence and dispersion. The test is built on a zero-inflated negative binomial regression model and winsorized count data to account for zero-inflation and outliers. Using simulated data and real microbiome sequencing datasets, we show that our test is robust across various biological conditions and overall more powerful than previous methods.Availability and implementationR package is available at https://github.com/jchen1981/MicrobiomeDDA.Supplementary informationSupplementary data are available at Bioinformatics online.

Motivation Our goal was to understand why the PLIER algorithm performs so well given its derivation is based on a biologically implausible assumption. Results In spite of a non-intuitive assumption regarding the PM and MM errors made as part of the derivation for PLIER, the resulting probe level error function does capture the key characteristics of the ideal error function, assuming MM probes only measure non-specific binding and no signal.

Abstract Background: Preclinical evidence indicates that increased insulin-like growth factor receptor-1 (IGF1R) signaling interferes with the action of trastuzumab suggesting a possible mechanism of trastuzumab resistance. Thus, we evaluated IGF1R prevalence, relationship with demographic data, and association with disease-free survival (DFS) of patients randomized to chemotherapy alone (Arm A) or chemotherapy with sequential (Arm B) or concurrent trastuzumab (Arm C) in the prospective phase III HER2+ adjuvant N9831 trial. Experimental Design: IGF1R protein expression was determined in tissue microarray sections (three cores per block; N = 1,197) or in whole tissue sections (WS; N = 537) using IHC (rabbit polyclonal antibody against IGF1R β-subunit). A tumor was considered positive (IGF1R+) if any core or WS had ≥1+ membrane staining in >0% invasive cells. Median follow-up was 8.5 years. Results: Of 1,734 patients, 708 (41%) had IGF1R+ breast tumors. IGF1R+ was associated with younger age (median 48 vs. 51, P = 0.007), estrogen receptor/progesterone receptor positivity (78% vs. 35%, P < 0.001), nodal positivity (89% vs. 83%, P < 0.001), well/intermediate grade (34% vs. 24%, P < 0.001), tumors ≥2 cm (72% vs. 67%, P = 0.02) but not associated with race or tumor histology. IGF1R did not affect DFS within arms. Between Arms A and C, patients with IGF1R+ and IGF1R− tumors had DFS HRs of 0.48 (P ≤ 0.001) and 0.68 (P = 0.009), respectively (Pinteraction = 0.17). Between Arms A and B, patients with IGF1R+ and IGF1R− tumors had DFS HRs of 0.83 (P = 0.25) and 0.69 (P = 0.01), respectively (Pinteraction = 0.42). Conclusions: In contrast to preclinical studies that suggest a decrease in trastuzumab sensitivity in IGF1R+ tumors, our adjuvant data show benefit of adding trastuzumab for patients with either IGF1R+ and IGF1R− breast tumors. Clin Cancer Res; 23(15); 4203–11. ©2016 AACR.

Doxorubicin and the ERBB2 targeted therapy, trastuzumab, are routinely used in the treatment of HER2+ breast cancer. In mouse models, doxorubicin is known to cause cardiomyopathy and conditional cardiac knock out of Erbb2 results in dilated cardiomyopathy and increased sensitivity to doxorubicin-induced cell death. In humans, these drugs also result in cardiac phenotypes, but severity and reversibility is highly variable. We examined the association of decline in left ventricular ejection fraction (LVEF) at 15,204 single nucleotide polymorphisms (SNPs) spanning 72 cardiomyopathy genes, in 800 breast cancer patients who received doxorubicin and trastuzumab. For 7033 common SNPs (minor allele frequency (MAF) > 0.01) we performed single marker linear regression. For all SNPs, we performed gene-based testing with SNP-set (Sequence) Kernel Association Tests: SKAT, SKAT-O and SKAT-common/rare under rare variant non-burden; rare variant optimized burden and non-burden tests; and a combination of rare and common variants respectively. Single marker analyses identified seven missense variants in OBSCN (p = 0.0045–0.0009, MAF = 0.18–0.50) and two in TTN (both p = 0.04, MAF = 0.22). Gene-based rare variant analyses, SKAT and SKAT-O, performed very similarly (ILK, TCAP, DSC2, VCL, FXN, DSP and KCNQ1, p = 0.042–0.006). Gene-based tests of rare/common variants were significant at the nominal 5% level for OBSCN as well as TCAP, DSC2, VCL, NEXN, KCNJ2 and DMD (p = 0.044–0.008). Our results suggest that rare and common variants in OBSCN, as well as in other genes, could have modifying effects in cardiomyopathy.