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Record W4292895541 · doi:10.1158/0008-5472.4287.72.17

Highlights from Recent Cancer Literature

2012· article· en· W4292895541 on OpenAlexaboutno aff

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
Fundersnot available
KeywordsCancerMedicineInternal medicine

Abstract

fetched live from OpenAlex

Wang R, Pan Y, Li C, Hu H, Zhang Y, Li H, et al. The use of quantitative real-time reverse transcriptase PCR for 50 and 30 portions of ALK transcripts to detect ALK rearrangements in lung cancers. Clin Cancer Res 2012;18:4725–32.Between 3% and 7% of non–small cell lung cancers (NSCLC) harbor an ALK fusion gene. Fusion of ALK with the upstream partner, EML4, was discovered in NSCLC in 2007. Since that time, additional fusion partners, including TFG and KIF5B, have been identified. The clinical importance of this finding is that crizotinib, an ALK inhibitor, is recommended in NSCLC, but only in the setting of an ALK fusion. The breakpoint in ALK occurs at a consistent location (exon 20) that can be utilized to facilitate detection. This breakpoint and fusion with another gene, such as EML4, lead to strong expression of ALK kinase domain and result in unequal expression of the 5′ and 3′ regions of the ALK transcript. Wang and colleagues take advantage of this property and measure ALK expression levels from regions on both sides of the exon 20 breakpoint. They use quantitative real-time reverse transcriptase PCR (qRT-PCR) to examine potential fusions in 177 NSCLCs. ALK FISH was used to confirm the accuracy of qRT-PCR, which was followed by RT-PCR and 5′ RACE to identify the fusion variants. The authors found perfect sensitivity and specificity (100% and 100%, respectively) for detection of ALK rearrangements in their specimens. Their method led to the identification of 6 novel ALK fusion variants, including 1 new KIF5B-ALK and 5 new EML4-ALK variants. By comparison, immunohistochemistry for ALK lacked sufficient sensitivity for clinical screening. Although FISH is the current gold standard, it requires several days and specialized expertise. The authors' use of real-time PCR, a staple in most molecular diagnostic laboratories, provides an additional option for screening of this important predictive marker.Delloye-Bourgeois C, Goldschneider D, Paradisi A, Therizols G, Belin S, Hacot S, et al. Nucleolar localization of a netrin-1 isoform enhances tumor cell proliferation. Sci Signal 2012;5:ra57.In recent years it has been shown that major oncogenic pathways such as Myc and PI3K–AKT–mTOR directly control the expression of many components that coordinate ribosome biogenesis during cellular transformation. These studies underscore the importance of controlling ribosomal RNA (rRNA) synthesis to promote cell growth and division. However, the molecular and cellular mechanisms by which alterations in ribosome biogenesis play a causal role in tumor formation are still poorly understood. Another outstanding and unresolved question is whether changes in ribosome number may be a general mechanism of cellular transformation, which is not only restricted to specific oncogenic signaling. Related to these questions, Delloye-Bourgeois and colleagues report that netrin-1, a laminin-related protein essential for proper axon guidance during neuronal development, might also play a role in tumor formation by increasing rRNA synthesis. Research into the tumorigenic role of netrin-1 has mainly focused on its extracellular function to suppress programmed cell death triggered by its transmembrane receptors, DCC (deleted in colorectal carcinoma) and the UNC5H family of receptors. Surprisingly, Delloye-Bourgeois and colleagues now show that cancer cells produce a distinct isoform of netrin-1 (ΔN-netrin-1) that localizes specifically in the nucleolus. The authors undertook a functional analysis of the netrin-1 protein and discovered that a domain located near the N-terminus of netrin-1 inhibits movement of netrin-1 to the nucleus (domain VI) while a region of the molecule known as the C-terminal C domain, which is largely dispensable for the secretory functions of netrin-1, including axon guidance and cell survival, is required for nucleolar localization. The mechanisms by which domain VI inhibits the entry of netrin-1 to the nucleus are poorly understood, but importantly, in cancer cells, ΔN-netrin-1 is encoded from an alternative promoter such that the repressive activity of domain VI is removed. The authors sought to uncover the functional relevance of ΔN-netrin-1 recruitment into the nucleolus. They determined that overexpressing or silencing ΔN-netrin-1 affects nucleolar structure. To investigate whether ΔN-netrin-1 may be implicated in rRNA biogenesis, the authors performed immunoprecipitation studies and showed that nucleolar netrin-1 interacts with key regulators of rRNA synthesis such as B23 and the rRNA transcription factor UBF. Furthermore, chromatin immunoprecipitation (ChIP) and pulse chase experiments revealed that the truncated netrin-1 is a member of the rRNA transcription complex in the nucleolus that directly interacts with the rRNA promoter to upregulate rRNA production and processing, leading to an expansion in the abundance of ribosomes in the cytoplasmic compartment. Delloye-Bourgeois and colleagues also showed that the nucleolar function of ΔN-netrin-1 promoted proliferation of IMR32 neuroblastoma cells as well as their ability to form foci in soft agar. They extended these data with xenograft experiments in the chorioallantoic membrane of chick embryos to show that ΔN-netrin-1 also possessed oncogenic potential in vivo. Finally, the authors reported that ΔN-netrin-1 is also expressed in human cancers. They show that, in a significant proportion of neuroblastomas as well as colon and pancreatic adenocarcinomas, ΔN-netrin-1 localizes in the nucleolus only in the cancer cells but not in the surrounding stroma or adjacent normal tissues. These overall findings strongly support the possibility that ΔN-netrin-1 may promote tumor formation by upregulating rRNA synthesis. It will be important to unravel the direct consequence of this mechanism; for example, does general protein synthesis or the translational control of specific mRNAs increase upon ΔN-netrin-1 expression? The results from these studies are also important for therapy, as major efforts are in place to target the survival capacity of netrin-1 in cancer, which will not be efficacious toward ΔN-netrin-1 and its activity in the nucleolus.Wolf MJ, Hoos A, Bauer J, Boettcher S, Knust M, Weber A, et al. Endothelial CCR2 signaling induced by colon carcinoma cells enables extravasation via the JAK2-Stat5 and p38MAPK pathway. Cancer Cell 2012;22:91–105.Chemokines and their receptors control the movement of cells into and out of tumor microenvironments. Good evidence exists to show that chemokine production by malignant cells determines the extent and phenotype of the leukocyte infiltrate and may also be involved in recruitment of mesenchymal and endothelial cells to the tumor mass. Wolf and colleagues have uncovered another reason why malignant cells secrete chemokines—to aid extravasation. These authors focused on the chemokine CCL2 that is secreted by many human and murine malignant cells and is abundant in most tumor microenvironments. Through their use of a range of cancer models in chimeric, transgenic, and knockout mice, as well as in vitro assays, the authors observed the following scenario: When the malignant cells arrest in blood vessels, CCL2-secreting malignant cells induce a local chemokine gradient that recruits monocytes expressing the CCL2 receptor, CCR2. This malignant cell CCL2 also activates CCR2 on the endothelium. The resulting activation of JAK2 and p38MAPK enhances vascular permeability. This increase in permeability, along with monocyte recruitment, enables efficient extravasation of the malignant cells. In human colon tumors, the authors found that levels of CCL2 transcripts were higher in all disease stages compared with healthy tissue but were particularly elevated in stage IV tumors with distant metastases. Several studies have already associated the CCR2/CCL2 axis with increased metastatic potential in other cancers. Increasing interest has been shown in chemokines and their receptors as targets for cancer treatment. This study further strengthens the case for chemokine and chemokine receptor antagonists as part of the repertoire of biologic treatments for cancer.Robinson G, Parker M, Kranenburg TA, Lu C, Chen X, Ding L, et al. Novel mutations target distinct subgroups of medulloblastoma. Nature 2012;488:43–8.Jones DT, Jäger N, Kool M, Zichner T, Hutter B, Sultan M, et al. Dissecting the genomic complexity underlying medulloblastoma. Nature 2012;488:100–5.Pugh TJ, Weeraratne SD, Archer TC, Pomeranz Krummel DA, Auclair D, Bochicchio J, et al. Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations. Nature 2012;488:106–10.Northcott PA, Shih DJ, Peacock J, Garzia L, Morrissy AS, Zichner T, et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 2012;488:49–56.Medulloblastoma is the most common malignant brain tumor of childhood. Tumors are comprised of 3 pathologic groups, classical (50%), nodular or desmoplastic (35%) and large cell/anaplastic (10%), and 4 transcriptomal groups: 2 driven by WNT or SHH signaling and 2 (groups 3 and 4) without clear driving mutations. Four recent articles in Nature representing a number of international research groups provide sequencing and copy number analyses of medulloblastoma tumors. Robinson and colleagues (United States) sequenced full genomes from 37 tumors and matched normal samples. New recurrent mutations included regulators of H3K27 and H3K4 trimethylation (KDM6A and ZMYM3) in groups 3 and 4, and CTNNB1-associated chromatin remodeling mutations (SMARCA4 and CREBBP) in the WNT subgroup. Modeling of mutations in mouse lower rhombic lip progenitors identified genes that maintained the WNT lineage (DDX3X), as well as mutated genes that initiated (CDH1) or cooperated (PIK3CA) in tumorigenesis. Jones and colleagues (Germany) sequenced full genomes from 125 tumor–normal pairs, identifying tetraploidy as a frequent event in group 3 and 4 tumors, new recurrent mutations in DDX3X, CTDNEP1, KDM6A, TBR1 (correlating with specific subgroups), and chromatin modifiers in all subgroups. RNA sequencing revealed new medulloblastoma fusion genes. Pugh and colleagues (United States) reported whole-exome hybrid capture and deep sequencing across the coding regions of 92 tumor–normal pairs. Medulloblastomas had a low mutation rate of 0.35 non-silent mutations per megabase. In addition to known mutations, recurrent somatic mutations were newly identified in the RNA helicase gene, DDX3X, often concurrent with CTNNB1 mutations, and in the nuclear co-repressor (N-CoR) complex genes GPS2, BCOR, and LDB1. Mutant DDX3X potentiated transactivation of a TCF promoter and enhanced cell viability in combination with mutant, but not wild-type, β-catenin. Northcott and colleagues (Canada) reported somatic copy number aberrations in over 1,000 tumors. The most common region of focal copy number gain was a tandem duplication of SNCAIP, a gene associated with Parkinson disease, which was restricted to group 4. Recurrent translocations of PVT1, including PVT1-MYC and PVT1-NDRG1, arose through chromothripsis and were restricted to group 3. Targetable abnormalities included recurrent events targeting TGF-β signaling in group 3 and NF-κB signaling in group 4, suggesting future avenues for rational, targeted therapy. These data provide new insights into the pathogenesis of medulloblastoma and highlight new potential therapeutic targets.Note: Breaking Advances are written by Cancer Research Editors. Readers are encouraged to consult the articles referred to in each item for full details on the findings described.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.131
Threshold uncertainty score0.438

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.1310.073

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.091
GPT teacher head0.490
Teacher spread0.399 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2012
Admission routes1
Has abstractyes

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