Case Report: Effectiveness of Targeted Treatment in a Patient With Pancreatic Cancer Harboring PALB2 Germline Mutation and KRAS Somatic Mutation

Pancreatic cancer is one of the most leading causes of cancer death worldwide. The rapid development of next-generation sequencing (NGS) and precision medicine promote us to seek potential targets for the treatment of pancreatic cancer. Here, we report a female pancreatic cancer patient who underwent radical surgical excision after neoadjuvant chemotherapy. After the surgery, the patient underwent gemcitabine + S-1 therapy, capecitabine + albumin paclitaxel therapy and irinotecan therapy successively, however, MRI review revealed tumor progression. The surgical tissue sample was subjected to next-generation sequencing (NGS), and PALB2 germline mutation and KRAS somatic mutation were identified. The patient then received olaparib (a PARP inhibitor) + irinotecan and the disease stabilized for one year. Due to the increased CA19-9, treatment of the patient with a combination of trametinib (a MEK inhibitor) and hydroxychloroquine resulted in stable disease (SD) with a significant decrease of CA19-9. This case demonstrated that the NGS may be a reliable method for finding potential therapeutic targets for pancreatic cancer.

Persistence of parental age effect on somatic mutation rates across generations in Arabidopsis.

In the model plant Arabidopsis thaliana, parental age is known to affect somatic mutation rates in their immediate progeny but it is not clear if this age-associated effect on mutation rates persist across successive generations. Using a set of detector lines carrying the mutated uidA gene, we examined if a particular parental age maintained across five consecutive generations affected the rates of base substitution (BSR), intrachromosomal recombination (ICR), frameshift mutation (FS), and transposition. The frequency of functional GUS (blue colored spots) reversions were examined in seedlings as a function of identical/different parental ages across generations. When parental age remained constant, no change was observed in BSR/ICR rates in the first three generations, following which it drops significantly in the 4th and in most instances, is elevated in the 5th generation. On the other hand, with advancing parental age, BSR/ICR rates respectively remained high in the first two/three generations with a striking resemblance in the pattern of mutation rates. We followed a novel approach of identifying and tagging flowers pollinated on a particular day, thereby avoiding possible emasculation induced stress responses, as it may influence mutation rates. By and large there is no correlation in the expression of candidate genes involved in DNA repair to the pattern of reversion events and possibly, the expression patterns may correspond to the genomewide somatic mutations rates. Our results suggest a time component in counting the number of generations a plant has passed through self-fertilization at a particular age in determining the somatic mutation rates.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Background Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. Results In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. Conclusions The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions

Multi-Omics Analysis Based on Genomic Instability for Prognostic Prediction in Lower-Grade Glioma

Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N6-methyladenosine (m6A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m6A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m6A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

Somatic Mutation of BAP1 Can Lead to Expression Loss in Non-Small Cell Lung Carcinoma: Next Generation Sequencing and IHC Analysis in A Large Single Institute Cohort

Introduction. As a tumor suppressor, germline and somatic inactivation of BRCA1 associated protein 1 gene ( BAP1) is a common finding in mesothelioma, melanocytic tumors, clear cell renal cell carcinoma and several other epithelial, mesenchymal and neural tumors. Incidence of BAP1 genetic alterations and subsequent expression loss has not been well established in non-small cell lung carcinoma (NSCLC) by large-scale studies. Design. After IRB approval, a total of 356 NSCLC cases of our institution between July 2016 and June 2020 were reviewed. The study cohort consisted of 214 (60%) adenocarcinomas, 89 (25%) squamous cell carcinomas, and 53 (15%) diagnosed as “non-small cell lung carcinoma” without specified subtype. All tumors underwent comprehensive target cancer gene next generation sequencing (Oncomine Assay). The protein expression status of BAP1 was subsequently evaluated by immunohistochemistry. Results. BAP1 somatic mutations were detected in 8 NSCLC tumors (incidence: 2.2%). Tumors harboring BAP1 mutations were all diagnosed at advanced stage and carried at least one additional genetic alteration. Immunohistochemically, four tumors showed complete loss of BAP1 protein expression, including two adenocarcinomas which harbored different missense BAP1 mutations and another two with bioinformatically predicated deleterious frameshifting mutations. Conclusion. Compared with known BAP1 loss associated other malignancies, such as mesothelioma, inactivation of BAP1 by somatic mutation is a rare occurrence in NSCLC. BAP1 mutations and loss of expression in NSCLC are accompanied by other complex genetic alternations, suggesting BAP1 mutation maybe a late event NSCLC carcinogenesis.

ROKET: Associating Somatic Mutation with Clinical Outcomes through Kernel Regression and Optimal Transport

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analyses is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess the joint association. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene-gene similarity defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least three cancer types harboring associations between somatic mutations and overall survival, progression-free interval or cytolytic activity.

Cancer Type Classification in Liquid Biopsies Based on Sparse Mutational Profiles Enabled through Data Augmentation and Integration

Identifying the cell of origin of cancer is important to guide treatment decisions. Machine learning approaches have been proposed to classify the cell of origin based on somatic mutation profiles from solid biopsies. However, solid biopsies can cause complications and certain tumors are not accessible. Liquid biopsies are promising alternatives but their somatic mutation profile is sparse and current machine learning models fail to perform in this setting. We propose an improved method to deal with sparsity in liquid biopsy data. Firstly, data augmentation is performed on sparse data to enhance model robustness. Secondly, we employ data integration to merge information from: (i) SNV density; (ii) SNVs in driver genes and (iii) trinucleotide motifs. Our adapted method achieves an average accuracy of 0.88 and 0.65 on data where only 70% and 2% of SNVs are retained, compared to 0.83 and 0.41 with the original model, respectively. The method and results presented here open the way for application of machine learning in the detection of the cell of origin of cancer from liquid biopsy data.

Surprising genetic and pathological findings in a patient with giant bilateral periadrenal tumours: PEComas and mutations of PTCH1 in Gorlin-Goltz syndrome

Gorlin-Goltz syndrome (GGS) or nevoid basal cell carcinoma syndrome is a rare tumour-overgrowth syndrome associated with multiple developmental anomalies and a wide variety of tumours. Here, we describe a case of a man aged 23 years with GGS with bilateral giant tumours adjacent to both adrenals that raised the suspicion of malignancy on imaging. Histological analysis of both surgically resected tumours revealed perivascular epitheloid cell tumours (PEComas) that were independent of the adrenals. Exome sequencing of the patient’s blood sample revealed a novel germline heterozygous frameshift mutation in the PTCH1 gene. As a second hit, a somatic five nucleotide long deletion in the PTCH1 gene was demonstrated in the tumour DNA of both PEComas. To the best of our knowledge, this is the first report on PEComa in GGS, and this finding also raises the potential relevance of PTCH1 mutations and altered sonic hedgehog signalling in PEComa pathogenesis. The presence of the same somatic mutation in the bilateral tumours might indicate the possibility of a postzygotic somatic mutation that along with the germline mutation of the same gene could represent an intriguing genetic phenomenon (type 2 segmental mosaicism).