Improved pairwise kinship analysis using massively parallel sequencing

In the present study, 67 individuals from two families were analyzed to explore the efficacy of the ForenSeq^™ DNA Signature Prep Kit for pairwise kinship analysis. Six types of pairwise relationships including 81 parent-offspring, 60 full siblings, 48 grandparent-grandchildren, 147 uncle/aunt-nephew/nieces, 97 first cousins and 190 non-relatives were generated from these two families and the corresponding likelihood ratio (LR) was calculated using either sequence-based or length-based STR genotype data (i.e., LR_sequence and LR_length). In addition, 10,000 pairs of different relationships were simulated to estimate the system powers of the STRs and SNPs in this panel. The results showed that 54, 9 and 5 additional alleles were observed based on sequence for 27 autosomal STRs, 24 Y-STRs and 7 X-STRs, respectively, compared to those based on length information and 11 novel alleles were identified. Five mutations were found for 58 STRs in 81 parent-offspring but no mutations were observed for SNPs. For 27 autosomal STR loci, the LRs were increased from 9.20, 7.87, 2.01, 2.07, 0.42 for log₁₀LR_length to 11.52, 10.12, 2.61, 2.60, 0.52 for log₁₀LR_sequence for paternity index (PI), full siblings index (FSI), grandparent-grandchild index (GI), uncle/aunt-nephew/niece index (UNI) and first cousins index (FCI), respectively. PI values for 94 SNPs separated more than those of 27 STRs if two individuals were non parent-offspring relatives. For the simulation study, the effectiveness was 1 for the parent-offspring relationship at the thresholds of t₁ = − 4 and t₂ = 4 and was 0.9998 for full siblings (t₁ = − 2, t₂ = 2). With an error rate of 0.42%, 93.02% of second degree relatives could be identified at the thresholds of t₁ = − 1 and t₂ = 1. However, the effectiveness was only 0.4300 for first cousins with a relatively high error rate of 2.68% (t₁ = − 1, t₂ = 1). In conclusion, STR typing according to the sequence information is more polymorphic, which increases the discrimination power for kinship testing. Compared to these 27 STR markers, 94 SNP markers in this panel have advantages in paternity testing especially when mutated STRs are involved or when a relative is an alleged parent. This panel is powerful enough to resolve paternity and full sibling testing. Most of the second degree relationships could be identified with low error rate while more markers are still needed for first cousins testing.     

Signature Informed Consent for Long-Term Opioid Therapy in Patients With Cancer: Perspectives of Patients and Providers

ContextSignature informed consent (SIC) is a part of a Veterans Health Administration ethics initiative for patient education and shared decision making with long-term opioid therapy (LTOT). Historically, patients with cancer-related pain receiving LTOT are exempt from this process.ObjectivesOur objective is to understand patients' and providers' perspectives on using SIC for LTOT in patients with cancer-related pain.MethodsSemistructured interviews with 20 opioid prescribers and 20 patients who were prescribed opioids at two large academically affiliated Veterans Health Administration Medical Centers. We used a combination of deductive and inductive approaches in content analysis to produce emergent themes.ResultsPotential advantages of SIC are that it can clarify and help patients comprehend LTOT risks and benefits, provide clear upfront boundaries and expectations, and involve the patient in shared decision making. Potential disadvantages of SIC include time delay to treatment, discouragement from recommended opioid use, and impaired trust in the patient-provider relationship. Providers and patients have misconceptions about the definition of SIC. Providers and patients question if SIC for LTOT is really informed consent. Providers and patients advocate for strategies to improve comprehension of SIC content. Providers had divergent perspectives on exemptions from SIC. Oncologists want SIC for LTOT to be tailored for patients with cancer.ConclusionProvider and patient interviews highlight various aspects about the advantages and disadvantages of requiring SIC for LTOT in cancer-related pain. Tailoring SIC for LTOT to be specific to cancer-related concerns and to have an appropriate literacy level are important considerations.     

Tumor microenvironment characterization identifies two lung adenocarcinoma subtypes with specific immune and metabolic state

The tumor microenvironment (TME) is a vital component of tumor tissue. Increasing evidence suggests their significance in predicting outcomes and guiding therapies. However, no studies have reported a systematic analysis of the clinicopathologic significance of TME in lung adenocarcinoma (LUAD). Here, we inferred tumor stromal cells in 1184 LUAD patients using computational algorithms based on bulk tumor expression data, and evaluated the clinicopathologic significance of stromal cells. We found LUAD patients showed heterogeneous abundance in stromal cells. Infiltration of stromal cells was influenced by clinicopathologic features, such as age, gender, smoking, and TNM stage. By clustering stromal cells, we identified 2 clinically and molecularly distinct LUAD subtypes with immune active and immune repressed features. The immune active subtype is characterized by repressed metabolism and repressed proliferation of tumor cells, while the immune repressed subtype is characterized by active metabolism and active proliferation of tumor cells. Differentially expressed gene analysis of the two LUAD subtypes identified an immune activation signature. To diagnose TME subtypes practically, we constructed a TME score using principal component analysis based on the immune activation signature. The TME score predicted TME subtypes effectively in 3 independent datasets with areas under the receiver operating characteristic curves of 0.960, 0.812, and 0.819, respectively. In conclusion, we proposed 2 clinically and molecularly distinct LUAD subtypes based on tumor microenvironment that could be valuable in predicting clinical outcome and guiding immunotherapy.     

Inhibiting Pathways Predicted From a Steroid Hormone Gene Signature Yields Synergistic Antitumor Effects in NSCLC

IntroductionMounting evidence supports a role for estrogen signaling in NSCLC progression. We previously reported a seven-gene signature that predicts prognosis in estrogen receptor β positive (ERβ+) NSCLC. The signature defines a network comprised of ER and human EGFR-2/3 (HER2/HER3) signaling.MethodsWe tested the efficacy of combining the pan-HER inhibitor, dacomitinib, with the estrogen antagonist, fulvestrant, in ERβ+ NSCLC models with differing genotypes. We assessed the potency of this combination on xenograft growth and survival of host mice, and the ability to reverse the gene signature associated with poor outcome.ResultsSynergy was observed between dacomitinib and fulvestrant in three human ERβ+ NSCLC models: 201T (wild-type EGFR), A549 (KRAS mutant), and HCC827 (EGFR 19 deletion) with combination indices of 0.1-0.6. The combination, but not single agents, completely reversed the gene signature associated with poor prognosis in a mechanism that is largely mediated by activator protein 1 downregulation. In vivo, the combination also induced tumor regression and reversed the gene signature. In HCC827 xenografts treated with the combination, survival of mice was prolonged after therapy discontinuation, tumors that recurred were less aggressive, and two mechanisms of HER inhibitor resistance involving c-Met activation and PTEN loss were blocked.ConclusionsThe combination of an ER blocker and a pan-HER inhibitor provides synergistic efficacy in different models of ERβ+ NSCLC. Our data support the use of this combination clinically, considering its ability to induce potent antitumor effects and produce a gene signature that predicts better clinical outcomes.     

Mutational Signature Analysis Reveals NTHL1 Deficiency to Cause a Multi-tumor Phenotype

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Colon Cancer Tumor Location Defined by Gene Expression May Disagree With Anatomic Tumor Location

BackgroundCancers of the right colon have been shown to differ from left-side colon cancers in prognosis, response to epithelial growth factor receptor inhibitors, microsatellite instability and BRAF mutation status, and other molecular characteristics. Clinical application of these differences will benefit from a deeper understanding of how tumor location defines and is defined by gene expression.Materials and MethodsThis study was carried out using Affymetrix microarray datasets (Cohort A: training set, n = 352; validation set, n = 519) and samples from The Cancer Genome Atlas Colon Adenocarcinoma database (Cohort B: n = 408), in which tumor location was reported. Gene expression patterns characteristic of tumor side were identified in a manner unbiased by statistical classification method.ResultsIn the Cohort A validation set, the anatomic locations of 75% of tumors agree with the locations predicted by gene expression (so-called genomic location), whereas 8% of tumors had genomic locations discordant with their anatomic locations, and 17% of tumors had ambiguous genomic locations. Genomic location was a better predictor of microsatellite instability, CpG island methylator phenotype status, and BRAF mutation status than anatomic location. Tumors with ambiguous genomic location were significantly (P = 1.3 × 10⁻⁷) more likely to have the mesenchymal consensus molecular subtype (40%) than those with a specific genomic location (18%). A genomic signature to predict genomic location was defined.ConclusionTumor location is increasingly considered in deciding treatment of a colon tumor. We showed that genomic location was superior to anatomic location as a predictor of molecular characteristics, suggesting that it may be a more accurate predictor of response.