Aberrations in Capicua (CIC) have recently been implicated as a negative prognostic factor in a multitude of cancer types through activation of the MAPK signalling cascade and derepression of ...oncogenic ETS transcription factors. The Ataxin-family protein ATXN1L has previously been reported to interact with CIC in developmental and disease contexts to facilitate the repression of CIC target genes. To further investigate this relationship, we performed functional in vitro studies utilizing ATXN1L
and CIC
human cell lines and characterized a reciprocal functional relationship between CIC and ATXN1L. Transcriptomic interrogation of the CIC-ATXN1-ATXN1L axis in low-grade glioma, prostate adenocarcinoma and stomach adenocarcinoma TCGA cohorts revealed context-dependent convergence of gene sets and pathways related to mitotic cell cycle and division. This study highlights the CIC-ATXN1-ATXN1L axis as a more potent regulator of the cell cycle than previously appreciated.
The driver landscape of sporadic chordoma Tarpey, Patrick S; Behjati, Sam; Young, Matthew D ...
Nature communications,
10/2017, Letnik:
8, Številka:
1
Journal Article
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Chordoma is a malignant, often incurable bone tumour showing notochordal differentiation. Here, we defined the somatic driver landscape of 104 cases of sporadic chordoma. We reveal somatic ...duplications of the notochordal transcription factor brachyury (T) in up to 27% of cases. These variants recapitulate the rearrangement architecture of the pathogenic germline duplications of T that underlie familial chordoma. In addition, we find potentially clinically actionable PI3K signalling mutations in 16% of cases. Intriguingly, one of the most frequently altered genes, mutated exclusively by inactivating mutation, was LYST (10%), which may represent a novel cancer gene in chordoma.Chordoma is a rare often incurable malignant bone tumour. Here, the authors investigate driver mutations of sporadic chordoma in 104 cases, revealing duplications in notochordal transcription factor brachyury (T), PI3K signalling mutations, and mutations in LYST, a potential novel cancer gene in chordoma.
Over the past few years, the alkylating agent temozolomide has become the standard-of-care therapy for patients with glioblastoma, the most common brain tumor. Recently, large-scale cancer genome ...sequencing efforts have identified a hypermutation phenotype and inactivating MSH6 mismatch repair gene mutations in recurrent, post-temozolomide glioblastomas, particularly those growing more rapidly during temozolomide treatment. This study aimed to clarify the timing and role of MSH6 mutations in mediating glioblastoma temozolomide resistance.
MSH6 sequence and microsatellite instability (MSI) status were determined in matched prechemotherapy and postchemotherapy glioblastomas identified by The Cancer Genome Atlas (TCGA) as having posttreatment MSH6 mutations. Temozolomide-resistant lines were derived in vitro through selective growth under temozolomide, and the MSH6 gene was sequenced in resistant clones. The role of MSH6 inactivation in mediating resistance was explored using lentiviral short hairpin RNA knockdown and MSH6 reconstitution.
MSH6 mutations were confirmed in posttreatment TCGA glioblastomas but absent in matched pretreatment tumors. The posttreatment hypermutation phenotype displayed a signature bias toward CpC transitions and was not associated with MSI. In vitro modeling through exposure of an MSH6 wild-type glioblastoma line to temozolomide resulted in resistant clones; one clone showed an MSH6 mutation, Thr(1219)Ile, that had been independently noted in two treated TCGA glioblastomas. Knockdown of MSH6 in the glioblastoma line U251 increased resistance to temozolomide cytotoxicity and reconstitution restored cytotoxicity in MSH6-null glioma cells.
MSH6 mutations are selected in glioblastomas during temozolomide therapy both in vitro and in vivo and are causally associated with temozolomide resistance.
Medulloblastoma is the most common pediatric malignant brain tumor. Although current therapies improve survival, these regimens are highly toxic and are associated with significant morbidity. Here, ...we report that placental growth factor (PlGF) is expressed in the majority of medulloblastomas, independent of their subtype. Moreover, high expression of PlGF receptor neuropilin 1 (Nrp1) correlates with poor overall survival in patients. We demonstrate that PlGF and Nrp1 are required for the growth and spread of medulloblastoma: PlGF/Nrp1 blockade results in direct antitumor effects in vivo, resulting in medulloblastoma regression, decreased metastasis, and increased mouse survival. We reveal that PlGF is produced in the cerebellar stroma via tumor-derived Sonic hedgehog (Shh) and show that PlGF acts through Nrp1—and not vascular endothelial growth factor receptor 1—to promote tumor cell survival. This critical tumor-stroma interaction—mediated by Shh, PlGF, and Nrp1 across medulloblastoma subtypes—supports the development of therapies targeting PlGF/Nrp1 pathway.
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► PlGF is expressed in medulloblastomas, regardless of their genetic subtype ► The majority of PlGF protein is secreted by cerebellar stromal cells ► Stromal PlGF production is stimulated by paracrine Shh secretion from cancer cells ► Targeting PlGF/Nrp1 pathway suppresses growth and spread of medulloblastoma
Placental growth factor (PlGF) and its receptor neuropilin 1 are required for the growth of medulloblastoma, the most common malignant brain tumor in children. Experiments in mice suggest that inhibition of PlGF or neuropilin may be an effective therapeutic strategy for suppressing the growth of these tumors.
In the past few decades, neuropathology has experienced several paradigm shifts with the introduction of new technologies. Deep learning, a rapidly progressing subfield of machine learning, seems to ...be the next innovation to alter the diagnostic workflow. In this review, we will explore the recent changes in the field of neuropathology and how this has led to an increased focus on molecular features in diagnosis and prognosis. Then, we will examine the work carried out to train deep learning models for various diagnostic tasks in neuropathology, as well as the machine learning frameworks they used. Focus will be given to both the challenges and successes highlighted therein, as well as what these trends may tell us about future roadblocks in the widespread adoption of this new technology. Finally, we will touch on recent trends in deep learning, as applied to digital pathology more generally, and what this may tell us about the future of deep learning applications in neuropathology.
Endometrial stromal sarcoma encompasses a heterogeneous group of uterine mesenchymal neoplasms, which are currently divided into low‐grade and high‐grade subtypes. Low‐grade endometrial stromal ...sarcoma is morphologically bland; molecularly, these tumors frequently contain JAZF1‐SUZ12, JAZF1‐PHF1, and EPC1‐PHF1 fusions. In contrast, high‐grade endometrial stromal sarcoma is characterized by morphologically undifferentiated neoplasms with high‐grade nuclear features; these tumors likewise appear to be genetically diverse with YWHAE‐NUTM2 and ZC3H7B‐BCOR representing the most frequent gene fusions. Herein, we describe two novel EPC1 fusion genes in endometrial stromal sarcoma: EPC1‐SUZ12 and EPC1‐BCOR. Both tumors were characterized be an aggressive clinical course.
Purpose
We hypothesized that combining multiple amorphous silicon flat panel layers increases photon detection efficiency in an electronic portal imaging device (EPID), improving image quality and ...tracking accuracy of low‐contrast targets during radiotherapy.
Methods
The prototype imager evaluated in this study contained four individually programmable layers each with a copper converter layer, Gd2O2S scintillator, and active‐matrix flat panel imager (AMFPI). The imager was placed on a Varian TrueBeam linac and a Las Vegas phantom programmed with sinusoidal motion (peak‐to‐peak amplitude = 20 mm, period = 3.5 s) was imaged at a frame rate of 10 Hz with one to four layers activated. Number of visible circles and CNR of least visible circle (depth = 0.5 mm, diameter = 7 mm) was computed to assess the image quality of single and multiple layers. A previously validated tracking algorithm was employed for auto‐tracking. Tracking error was defined as the difference between the programmed and tracked positions of the circle. Pearson correlation coefficient (R) of CNR and tracking errors was computed.
Results
Motion‐induced blurring significantly reduced circle visibility. During four cycles of phantom motion, the number of visible circles varied from 11–23, 13–24, 15–25, and 16–26 for one‐, two‐, three‐, and four‐layer imagers, respectively. Compared with using only a single layer, combining two, three, and four layers increased the median CNR by factors of 1.19, 1.42, and 1.71, respectively and reduced the average tracking error from 3.32 mm to 1.67 mm to 1.47 mm, and 0.74 mm, respectively. Significant correlations (P~10−9) were found between the tracking error and CNR.
Conclusion
Combination of four conventional EPID layers significantly improves the EPID image quality and tracking accuracy for a poorly visible object which is moving with a frequency and amplitude similar to respiratory motion.
Purpose
To study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict ...nodule malignancy.
Methods
We obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi‐automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm.
Results
We find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was 0.15 to 0.86) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of 0.54 to 0.93 among the algorithms, while percent change in volume was 0.3 to 0.95. Compared to that of smaller nodules which had a range of −0.0038 to 0.69 and percent change in volume was −0.039 to 0.92. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77).
Conclusions
We find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams).
Structural variants (SVs) may be an underestimated cause of hereditary cancer syndromes given the current limitations of short-read next-generation sequencing. Here we investigated the utility of ...long-read sequencing in resolving germline SVs in cancer susceptibility genes detected through short-read genome sequencing.
Known or suspected deleterious germline SVs were identified using Illumina genome sequencing across a cohort of 669 advanced cancer patients with paired tumor genome and transcriptome sequencing. Candidate SVs were subsequently assessed by Oxford Nanopore long-read sequencing.
Nanopore sequencing confirmed eight simple pathogenic or likely pathogenic SVs, resolving three additional variants whose impact could not be fully elucidated through short-read sequencing. A recurrent sequencing artifact on chromosome 16p13 and one complex rearrangement on chromosome 5q35 were subsequently classified as likely benign, obviating the need for further clinical assessment. Variant configuration was further resolved in one case with a complex pathogenic rearrangement affecting TSC2.
Our findings demonstrate that long-read sequencing can improve the validation, resolution, and classification of germline SVs. This has important implications for return of results, cascade carrier testing, cancer screening, and prophylactic interventions.
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