Senescence is a sequence of biochemical and physiological events that constitute the final stage of development. The identification of genes that alter senescence has practical value and is helpful in revealing pathways that influence senescence. However, the genetic mechanisms of senescence are largely unknown. The leaf of the oresara9 (ore9) mutant of Arabidopsis exhibits increased longevity during age-dependent natural senescence by delaying the onset of various senescence symptoms. It also displays delayed senescence symptoms during hormone-modulated senescence. Map-based cloning of ORE9 identified a 693-amino acid polypeptide containing an F-box motif and 18 leucinerich repeats. The F-box motif of ORE9 interacts with ASK1 (Arabidopsis Skp1-like 1), a component of the plant SCF complex. These results suggest that ORE9 functions to limit leaf longevity by removing, through ubiquitin-dependent proteolysis, target proteins that are required to delay the leaf senescence program in Arabidopsis.

Motivation: Identifying altered pathways in an individual is important for understanding disease mechanisms and for the future application of custom therapeutic decisions. Existing pathway analysis techniques are mainly focused on discovering altered pathways between normal and cancer groups and are not suitable for identifying the pathway aberrance that may occur in an individual sample. A simple way to identify individual’s pathway aberrance is to compare normal and tumor data from the same individual. However, the matched normal data from the same individual are often unavailable in clinical situation. Therefore, we suggest a new approach for the personalized identification of altered pathways, making special use of accumulated normal data in cases when a patient’s matched normal data are unavailable. The philosophy behind our method is to quantify the aberrance of an individual sample's pathway by comparing it with accumulated normal samples. We propose and examine personalized extensions of pathway statistics, overrepresentation analysis and functional class scoring, to generate individualized pathway aberrance score.Results: Collected microarray data of normal tissue of lung and colon mucosa are served as reference to investigate a number of cancer individuals of lung adenocarcinoma (LUAD) and colon cancer, respectively. Our method concurrently captures known facts of cancer survival pathways and identifies the pathway aberrances that represent cancer differentiation status and survival. It also provides more improved validation rate of survival-related pathways than when a single cancer sample is interpreted in the context of cancer-only cohort. In addition, our method is useful in classifying unknown samples into cancer or normal groups. Particularly, we identified ‘amino acid synthesis and interconversion’ pathway is a good indicator of LUAD (Area Under the Curve (AUC) 0.982 at independent validation). Clinical importance of the method is providing pathway interpretation of single cancer, even though its matched normal data are unavailable.Availability and implementation: The method was implemented using the R software, available at our Web site: http://bibs.snu.ac.kr/ipas.Contact: tspark@stat.snu.ac.kr or namhuh@samsung.comSupplementary information: Supplementary data are available at Bioinformatics online.

Senescence is a sequence of biochemical and physiological events that constitute the final stage of development. The identification of genes that alter senescence has practical value and is helpful in revealing pathways that influence senescence. However, the genetic mechanisms of senescence are largely unknown. The leaf of the oresara9 (ore9) mutant of Arabidopsis exhibits increased longevity during age-dependent natural senescence by delaying the onset of various senescence symptoms. It also displays delayed senescence symptoms during hormone-modulated senescence. Map-based cloning of ORE9 identified a 693-amino acid polypeptide containing an F-box motif and 18 leucine-rich repeats. The F-box motif of ORE9 interacts with ASK1 (Arabidopsis Skp1-like 1), a component of the plant SCF complex. These results suggest that ORE9 functions to limit leaf longevity by removing, through ubiquitin-dependent proteolysis, target proteins that are required to delay the leaf senescence program in Arabidopsis.

BackgroundIn this study, we established patient-derived tumor cell (PDC) models using tissues collected from patients with metastatic cancer and assessed whether these models could be used as a tool for genome-based cancer treatment.MethodsPDCs were isolated and cultured from malignant effusions including ascites and pleural fluid. Pathological examination, immunohistochemical analysis, and genomic profiling were performed to compare the histological and genomic features of primary tumors, PDCs. An exploratory gene expression profiling assay was performed to further characterize PDCs.ResultsFrom January 2012 to May 2013, 176 samples from patients with metastatic cancer were collected. PDC models were successfully established in 130 (73.6%) samples. The median time from specimen collection to passage 1 (P1) was 3 weeks (range, 0.5–4 weeks), while that from P1 to P2 was 2.5 weeks (range, 0.5–5 weeks). Sixteen paired samples of genomic alterations were highly concordant between each primary tumor and progeny PDCs, with an average variant allele frequency (VAF) correlation of 0.878. We compared genomic profiles of the primary tumor (P0), P1 cells, P2 cells, and patient-derived xenografts (PDXs) derived from P2 cells and found that three samples (P0, P1, and P2 cells) were highly correlated (0.99–1.00). Moreover, PDXs showed more than 100 variants, with correlations of only 0.6–0.8 for the other samples. Drug responses of PDCs were reflective of the clinical response to targeted agents in selected patient PDC lines.Conclusion(s)Our results provided evidence that our PDC model was a promising model for preclinical experiments and closely resembled the patient tumor genome and clinical response.

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BackgroundFibroblast growth factor 2 (FGFR2) amplification, occurring in ~2–9% of gastric cancers (GC), is associated with poor overall survival.ResultsRNA sequencing identified a novel FGFR2-ACSL5 fusion in the resistant tumor that was absent from the matched pre-treatment tumor. The FGFR2-amplified PDC line was sensitive to FGFR inhibitors whereas the PDC line with concomitant FGFR2 amplification and FGFR2-ACSL5 fusion exhibited resistance. Additionally, the FGFR2-amplified GC PDC line, which was initially sensitive to FGFR2 inhibitors, subsequently also developed resistance.Materials and MethodsWe identified an FGFR2-amplified patient with GC, who demonstrated a dramatic and long-term response to LY2874455, a pan-FGFR inhibitor, but eventually developed an acquired LY2874455 resistance. Following resistance development, an endoscopic biopsy was performed for transcriptome sequencing and patient-derived tumor cell line (PDC) establishment to elucidate the underlying molecular alterations.ConclusionsFGFR inhibitors may function against FGFR2-amplified GC, and a novel FGFR2-ACSL5 fusion identified by transcriptomic characterization may underlie clinically acquired resistance.Implications for PracticePoor treatment response represents a substantial concern in patients with gastric cancer carrying multiple FGFR2 gene copies. Here, we show the utility of a general FGFR inhibitor for initial response prior to treatment resistance and report the first characterization of a potential resistance mechanism involving an FGFR2-ACSL5 fusion protein.

In women with metastatic breast cancer (MBC), introduction of the anti-HER2 (human epidermal growth factor receptor-2) directed therapies including trastuzumab, pertuzumab, lapatinib, and/or trastuzumab-DM1 has markedly improved overall survival. However, not all cases of HER2-positive breast tumours derive similar benefit from HER2-directed therapy, and a significant number of patients experience disease progression because of primary or acquired resistance to anti-HER2-directed therapies. We integrated genomic and clinicopathological analyses in a cohort of patients with refractory breast cancer to anti-HER2 therapies to identify the molecular basis for clinical heterogeneity. To study the molecular basis underlying refractory MBC, we obtained 36 MBC tumours tissues and used next-generation sequencing to investigate the mutational and transcriptional profiles of 83 genes. We focused on HER2 mutational sites and HER2 pathways to identify the roles of HER2 mutations and the HER2 pathway in the refractoriness to anti-HER2 therapies. Analysis using massively parallel sequencing platform, CancerSCAN™, revealed that HER2 mutations were found in six of 36 patients (16.7%). One patient was ER (estrogen receptor)-positive and HER2-negative and the other five HER2 mutated patients were HER2-positive and HR (hormone receptor)-negative. Most importantly, four of these five patients did not show any durable clinical response to HER2-directed therapies. The HER2 pathway score obtained through transcriptional analyses identified that Growth Receptor Biding protein 2 (GRB2) was the most significantly down regulated gene in the HER2 mutated samples. Detection of HER2 mutations using higher deep DNA sequencing may identify a predictive biomarker of resistance to HER2-directed therapy. Functional validation is warranted.

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecular "fingerprints", along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.