Highlights d Proteomic subgroups stratify patient survival and allocate specific treatments d Alterations of the liver-specific proteome and metabolism in HCC are identified d Multi-omics profile of key signaling and metabolic pathways in HCC is depicted d CTNNB1 mutation-associated ALDOA phosphorylation promotes HCC cell proliferation
Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
Background: In recent years, the maturation of microarray technology has allowed the genomewide analysis of gene expression patterns to identify tissue-specific and ubiquitously expressed ('housekeeping') genes. We have performed a functional and topological analysis of housekeeping and tissue-specific networks to identify universally necessary biological processes, and those unique to or characteristic of particular tissues.
A minority of the 1% to 9% IHC ER-positive tumors show molecular features similar to those of ER-positive, potentially endocrine-sensitive tumors, whereas most show ER-negative, basal-like molecular characteristics. The safest clinical approach may be to use both adjuvant endocrine therapy and chemotherapy in this rare subset of patients.
IntroductionAs part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints.MethodsWe used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set.ResultsA ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models.ConclusionsWe showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.
Background: A therapeutic strategy involving combined treatment with lenvatinib plus pembrolizumab (LEP) has demonstrated a relatively high antitumor response in several solid tumors; however, the efficacy and safety of LEP in patients with refractory bile tract carcinoma (BTC) remains unknown.Methods: This is a single-arm study for a preliminary assessment of the efficacy and tolerability of LEP in patients who experienced progression from prior systemic treatments. Pre-treatment tumor tissues were collected to retrospectively evaluate the expression status of PDL1.Results: Thirty-two patients received second-line and above treatment with LEP. Overall, the objective response rate (ORR) was 25%, the disease control rate (DCR) was 78.1%, and the clinical benefit rate (CBR) was 40.5%. The median progression-free survival (PFS) was 4.9 months (95% CI: 4.7-5.2 months), and the median overall survival (OS) was 11.0 months (95% CI: 9.6-12.3 months). For tolerability, no grade 5 serious adverse events (AEs) were reported. All patients had any-grade AEs, and 59.3% of the patients experienced grade 3 AEs, while only 1 patient experienced a grade 4 AE of stomach bleeding. Fatigue was the most common AE, followed by hypertension and elevated aminotransferase levels. Retrospective analysis for PDL1 expression revealed that PDL1 positive tumor cells were associated with improved clinical benefits and survival outcomes.Conclusions: LEP is a promising alternative as a non-first-line therapeutic regimen for patients with refractory BTC. Furthermore, well-designed prospective clinical trials with a control arm are still needed to obtain more evidences to confirm the efficacy and safety of this particular regimen as well as the role of PDL1 expression.
Background. Incorporation of next-generation sequencing (NGS) technology into clinical utility in targeted and immunotherapies requires stringent validation, including the assessment of tumor mutational burden (TMB) and microsatellite instability (MSI) status by NGS as important biomarkers for response to immune checkpoint inhibitors. Materials and Methods. We designed an NGS assay, Cancer Sequencing YS panel (CSYS), and applied algorithms to detect five classes of genomic alterations and two genomic features of TMB and MSI. Results. By stringent validation, CSYS exhibited high sensitivity and predictive positive value of 99.7% and 99.9%, respectively, for single nucleotide variation; 100% and 99.9%, respectively, for short insertion and deletion (indel); and 95.5% and 100%, respectively, for copy number alteration (CNA). Moreover, CSYS achieved 100% specificity for both long indel (50-3,000 bp insertion and deletion) and gene rearrangement. Overall, we used 33 cell lines and
SUMMARYMulti-region sequencing is used to detect intratumor genetic heterogeneity (ITGH) in tumors. To assess whether genuine ITGH can be distinguished from sequencing artifacts, we performed whole-exome sequencing (WES) on three anatomically distinct regions of the same tumor with technical replicates to estimate technical noise. Somatic variants were detected with three different WES pipelines and subsequently validated by high-depth amplicon sequencing. The cancer-only pipeline was unreliable, with about 69% of the identified somatic variants being false positive. Even with matched normal DNA for which 82% of the somatic variants were detected reliably, only 36%–78% were found consistently in technical replicate pairs. Overall, 34%–80% of the discordant somatic variants, which could be interpreted as ITGH, were found to constitute technical noise. Excluding mutations affecting low-mappability regions or occurring in certain mutational contexts was found to reduce artifacts, yet detection of sub-clonal mutations by WES in the absence of orthogonal validation remains unreliable.
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