Background: The kinship2 package is restructured from the previous kinship package. Existing features are now enhanced and new features added for handling pedigree objects. Methods: Pedigree plotting features have been updated to display features on complex pedigrees while adhering to pedigree plotting standards. Kinship matrices can now be calculated for the X chromosome. Other methods have been added to subset and trim pedigrees while maintaining the pedigree structure. Conclusion: We make the kinship2 package available for R on the Contributed R Archives Network (CRAN), where data management is built-in and other packages can use the pedigree object.

Background: Patient-derived xenografts (PDXs) are increasingly used in cancer research as a tool to inform cancer biology and drug response. Most available breast cancer PDXs have been generated in the metastatic setting. However, in the setting of operable breast cancer, PDX models both sensitive and resistant to chemotherapy are needed for drug development and prospective data are lacking regarding the clinical and molecular characteristics associated with PDX take rate in this setting.Methods: TheBreast Cancer Genome GuidedTherapy Study(BEAUTY) is a prospective neoadjuvant chemotherapy (NAC) trial of stage I-III breast cancer patients treated with neoadjuvant weekly taxane+/-trastuzumab followed by anthracycline-based chemotherapy. Using percutaneous tumor biopsies (PTB), we established and characterized PDXs from both primary (untreated) and residual (treated) tumors. Tumor take rate was defined as percent of patients with the development of at least one stably transplantable (passed at least for four generations) xenograft that was pathologically confirmed as breast cancer.Results: Baseline PTB samples from 113 women were implanted with an overall take rate of 27.4% (31/113). By clinical subtype, the take rate was 51.3% (20/39) in triple negative (TN) breast cancer, 26.5% (9/34) in HER2+, 5.0% (2/40) in luminal B and 0% (0/3) in luminal A. The take rate for those with pCR did not differ from those with residual disease in TN (p = 0.999) and HER2+ (p = 0.2401) tumors. The xenografts from 28 of these 31 patients were such that at least one of the xenografts generated had the same molecular subtype as the patient. Among the 35 patients with residual tumor after NAC adequate for implantation, the take rate was 17.1%. PDX response to paclitaxel mirrored the patients’ clinical response in all eight PDX tested.Conclusions: The generation of PDX models both sensitive and resistant to standard NAC is feasible and these models exhibit similar biological and drug response characteristics as the patients’ primary tumors. Taken together, these models may be useful for biomarker discovery and future drug development.

Purpose: Programmed death ligand 1 [PD-(L)1]-targeted therapies have shown modest survival benefit in triple-negative breast cancer (TNBC). PD-L1+ microenvironments in TNBC are not well characterized and may inform combinatorial immune therapies. Herein, we characterized clinicopathologic features, RNA-based immune signatures, and spatially defined protein-based tumor–immune microenvironments (TIME) in early-stage PD-L1+ and PD-L1− TNBC.Experimental Design: From a large cohort of chemotherapy-naïve TNBC, clinicopathologic features, deconvoluted RNA immune signatures, and intraepithelial and stromal TIME (Nanostring GeoMX) were identified in subsets of PD-L1+ and PD-L1− TNBC, as defined by FDA-approved PD-L1 companion assays.Results: 228 of 499 (46%) TNBC were PD-L1+ (SP142: ≥1% immune cells-positive). Using PD-L1 22C3, 46% had combined positive score (CPS) ≥ 1 and 16% had CPS ≥10. PD-L1+ TNBC were higher grade with higher tumor-infiltrating lymphocytes (TIL; P < 0.05). PD-L1 was not associated with improved survival following adjustment for TILs and other variables. RNA profiles of PD-L1+ TNBC had increased dendritic cell, macrophage, and T/B cell subset features; and decreased myeloid-derived suppressor cells. PD-L1+ stromal and intraepithelial TIMEs were highly enriched in IDO-1, HLA-DR, CD40, and CD163 compared with PD-L1-TIME, with spatially specific alterations in CTLA-4, Stimulator of Interferon Genes (STING), and fibronectin. Macrophage- and antigen presentation–related proteins correlated most strongly with PD-L1 protein.Conclusions: In this early-stage TNBC cohort, nearly 50% were PD-L1+ (SP142 companion assay) while 16% were PD-L1+ with the 22C3 companion assay. PD-L1+ TNBC had specific myeloid-derived and lymphoid features. Spatially defined PD-L1+ TIME were enriched in several clinically actionable immune proteins. These data may inform future studies on combinatorial immunotherapies for patients with PD-L1+ TNBC.See related commentary by Symmans, p. 5446

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant clonal disorder that progresses to multiple myeloma (MM), or other plasma-cell or lymphoid disorders at a rate of 1%/year. We evaluate the contribution of body mass index (BMI) to MGUS progression beyond established clinical factors in a population-based study. We identified 594 MGUS through a population-based screening study in Olmsted County, Minnesota, between 1995 and 2003. Follow-up time was calculated from the date of MGUS to last follow-up, death, or progression to MM/another plasma-cell/lymphoid disorder. BMI (kg/m² < 25/≥25) was measured close to screening date. We used Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association of BMI ≥ 25 versus BMI < 25 with MGUS progression and also evaluated the corresponding c-statistic and 95% CI to describe discrimination of the model for MGUS progression. Median follow-up was 10.5 years (range:0–25), while 465 patients died and 57 progressed and developed MM (N = 39), AL amyloidosis (N = 8), lymphoma (N = 5), or Waldenstrom-macroglobulinemia (N = 5). In univariate analyses, BMI ≥ 25 (HR = 2.14,CI:1.05–4.36, P = 0.04), non-IgG (HR = 2.84, CI:1.68–4.80, P = 0.0001), high monoclonal (M) protein (HR = 2.57, CI:1.50–4.42, P = 0.001), and abnormal free light chain ratio (FLC_r) (HR = 3.39, CI:1.98–5.82, P < 0.0001) were associated with increased risk of MGUS progression, and were independently associated in a multivariable model (c-statistic = 0.75, CI:0.68–0.82). The BMI association was stronger among females (HR = 3.55, CI:1.06–11.9, P = 0.04) vs. males (HR = 1.39, CI:0.57–3.36, P = 0.47), although the interaction between BMI and sex was not significant (P = 0.15). In conclusion, high BMI is a prognostic factor for MGUS progression, independent of isotype, M protein, and FLC_r. This association may be stronger among females.

Random forest (RF) analysis of genetic data does not require specification of the mode of inheritance, and provides measures of variable importance that incorporate interaction effects. In this paper we describe RF-based approaches for assessment of gene and haplotype importance, and apply these approaches to a subset of the North American Rheumatoid Arthritis Consortium case-control data provided by Genetic Analysis Workshop 16. The RF analyses of 37 genes identified many of the same genes as logistic regression, but also suggested importance of certain single-nucleotide polymorphism and genes that were not ranked highly by logistic regression. A new permutation method did not reveal strong evidence of gene-gene interaction effects in these data. Although RFs are a promising approach for genetic data analysis, extensions beyond simple single-nucleotide polymorphism analyses and modifications to improve computational feasibility are needed.

Several methods have been proposed to impute genotypes at untyped markers using observed genotypes and genetic data from a reference panel. We used the Genetic Analysis Workshop 16 rheumatoid arthritis case-control dataset to compare the performance of four of these imputation methods: IMPUTE, MACH, PLINK, and fastPHASE. We compared the methods' imputation error rates and performance of association tests using the imputed data, in the context of imputing completely untyped markers as well as imputing missing genotypes to combine two datasets genotyped at different sets of markers. As expected, all methods performed better for single-nucleotide polymorphisms (SNPs) in high linkage disequilibrium with genotyped SNPs. However, MACH and IMPUTE generated lower imputation error rates than fastPHASE and PLINK. Association tests based on allele "dosage" from MACH and tests based on the posterior probabilities from IMPUTE provided results closest to those based on complete data. However, in both situations, none of the imputation-based tests provide the same level of evidence of association as the complete data at SNPs strongly associated with disease.