EGFR and ERBB2 exon 20 insertion/duplication in advanced non–small cell lung cancer: genomic profiling and clinicopathologic features

BackgroundExon 20 (ex20) in-frame insertions or duplications (ins/dup) in epidermal growth factor receptor (EGFR) and its analog erb-b2 receptor tyrosine kinase 2 (ERBB2) are each detected in 1.5% of non–small cell lung cancer (NSCLC). Unlike EGFR p.L858R or ex19 deletions, ex20 ins/dup is associated with de novo resistance to classic EGFR inhibitors, lack of response to immune checkpoint inhibitors, and poor prognosis. US Food and Drug Administration has approved mobocertinib and amivantamab for targeting tumors with this aberration, but the number of comprehensive studies on ex20 ins/dup NSCLC is limited. We identified 18 cases of NSCLCs with EGFR/ERBB2 ex20 ins/dup and correlated the findings with clinical and morphologic information including programed death-ligand 1 (PD-L1) expression.MethodsA total of 536 NSCLC cases tested at our institution between 2014 and 2023 were reviewed. A custom-designed 214-gene next-generation sequencing panel was used for detecting DNA variants, and the FusionPlex CTL panel (ArcherDx) was used for the detection of fusion transcripts from formalin-fixed, paraffin-embedded tissue. Immunohistochemistry (IHC)for PD-L1 was performed using 22C3 or E1L3N clones.ResultsNine EGFR and nine ERBB2 ex20 ins/dup variants were identified from an equal number of men and women, 14 were non- or light smokers, and 15 had stage IV disease. All 18 cases were adenocarcinomas. Seven of the 11 cases with available primary tumors had acinar predominant pattern, two had lepidic predominant pattern, and the remainder had papillary (one case) and mucinous (one case) patterns. Ex20 ins/dup variants were heterogenous in-frame one to four amino acids spanning A767–V774 in EGFR and Y772–P780 in ERBB2 and were clustered in the loop following the C-helix and α C-helix. Twelve cases (67%) had co-existing TP53 variants. Copy number variation in CDK4 amplification was identified in one case. No fusion or microsatellite instability was identified in any case. PD-L1 was positive in two cases, low positive in four cases, and negative in 11 cases.ConclusionsNSCLCs harboring EGFR/ERBB2 ex20 ins/dup are rare and tend to be acinar predominant, negative for PD-L1, more frequent in non- or light smokers, and mutually exclusive with other driver mutations in NSCLC. The correlation of different EGFR/ERBB2 ex20 ins/dup variants and co-existing mutations with response to targeted therapy and the possibility of developing resistant mutations after mobocertinib treatment warrants further investigation

TRAPPC11 and GOSR2 mutations associate with hypoglycosylation of α-dystroglycan and muscular dystrophy

Abstract Background Transport protein particle (TRAPP) is a supramolecular protein complex that functions in localizing proteins to the Golgi compartment. The TRAPPC11 subunit has been implicated in muscle disease by virtue of homozygous and compound heterozygous deleterious mutations being identified in individuals with limb girdle muscular dystrophy and congenital muscular dystrophy. It remains unclear how this protein leads to muscle disease. Furthermore, a role for this protein, or any other membrane trafficking protein, in the etiology of the dystroglycanopathy group of muscular dystrophies has yet to be found. Here, using a multidisciplinary approach including genetics, immunofluorescence, western blotting, and live cell analysis, we implicate both TRAPPC11 and another membrane trafficking protein, GOSR2, in α-dystroglycan hypoglycosylation. Case presentation Subject 1 presented with severe epileptic episodes and subsequent developmental deterioration. Upon clinical evaluation she was found to have brain, eye, and liver abnormalities. Her serum aminotransferases and creatine kinase were abnormally high. Subjects 2 and 3 are siblings from a family unrelated to subject 1. Both siblings displayed hypotonia, muscle weakness, low muscle bulk, and elevated creatine kinase levels. Subject 3 also developed a seizure disorder. Muscle biopsies from subjects 1 and 3 were severely dystrophic with abnormal immunofluorescence and western blotting indicative of α-dystroglycan hypoglycosylation. Compound heterozygous mutations in TRAPPC11 were identified in subject 1: c.851A>C and c.965+5G>T. Cellular biological analyses on fibroblasts confirmed abnormal membrane trafficking. Subject 3 was found to have compound heterozygous mutations in GOSR2: c.430G>T and c.2T>G. Cellular biological analyses on fibroblasts from subject 3 using two different model cargo proteins did not reveal defects in protein transport. No mutations were found in any of the genes currently known to cause dystroglycanopathy in either individual. Conclusion Recessive mutations in TRAPPC11 and GOSR2 are associated with congenital muscular dystrophy and hypoglycosylation of α-dystroglycan. This is the first report linking membrane trafficking proteins to dystroglycanopathy and suggests that these genes should be considered in the diagnostic evaluation of patients with congenital muscular dystrophy and dystroglycanopathy

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation