ß-Catenin maintains lung epithelial progenitors after lung specification.

The entire lung epithelium arises from SRY box 9 (SOX9)-expressing progenitors that form the respiratory tree and differentiate into airway and alveolar cells. Despite progress in understanding their initial specification within the embryonic foregut, how these progenitors are subsequently maintained is less clear. Using inducible, progenitor-specific genetic mosaic mouse models, we showed that ß-catenin (CTNNB1) maintains lung progenitors by promoting a hierarchical lung progenitor gene signature, suppressing gastrointestinal (GI) genes, and regulating NK2 homeobox 1 (NKX2.1) and SRY box 2 (SOX2) in a developmental stage-dependent manner. At the early, but not later, stage post-lung specification, CTNNB1 cell-autonomously maintained normal NKX2.1 expression levels and suppressed ectopic SOX2 expression. Genetic epistasis analyses revealed that CTNNB1 is required for fibroblast growth factor (Fgf)/Kirsten rat sarcoma viral oncogene homolog (Kras)-mediated promotion of the progenitors. In silico screening of Eurexpress and translating ribosome affinity purification (TRAP)-RNAseq identified a progenitor gene signature, a subset of which depends on CTNNB1. Wnt signaling also maintained NKX2.1 expression and suppressed GI genes in cultured human lung progenitors derived from embryonic stem cells.

Mining the pre-diagnostic antibody repertoire of TgMMTV-neu mice to identify autoantibodies useful for the early detection of human breast cancer.

BACKGROUND: The use of autoantibodies for the early detection of breast cancer has generated much interest as antibodies can be readily assayed in serum when antigen levels are low. Ideally, diagnostic autoantibodies would be identified in individuals who harbored pre-invasive disease/high risk lesions leading to malignancy. Prospectively collected human serum samples from these individuals are rare and not often available for biomarker discovery. We questioned whether transgenic animals could be used to identify cancer-associated autoantibodies present at the earliest stages of the malignant transformation of breast cancer. METHODS: We collected sera from transgenic mice (TgMMTV-neu) from the time of birth to death by spontaneous mammary tumors. Using sera from a time point prior to the development of tumor, i.e. "pre-diagnostic", we probed cDNA libraries derived from syngeneic tumors to identify proteins recognized by IgG antibodies. Once antigens were identified, selected proteins were evaluated via protein arrays, for autoantibody responses using plasma from women obtained prior to the development of breast cancer and matched controls. The ability of the antigens to discriminate cases from controls was assessed using receiver-operating-characteristic curve analyses and estimates of the area under the curve. RESULTS: We identified 6 autoantibodies that were present in mice prior to the development of mammary cancer: Pdhx, Otud6b, Stk39, Zpf238, Lgals8, and Vps35. In rodent validation cohorts, detecting both IgM and IgG antibody responses against a subset of the identified proteins could discriminate pre-diagnostic sera from non-transgenic control sera with an AUC of 0.924. IgG and IgM autoantibodies, specific for a subset of the identified antigens, could discriminate the samples of women who eventually developed breast cancer from case-matched controls who did not develop disease. The discriminatory potential of the pre-diagnostic autoantibodies was enhanced if plasma samples were collected greater than 5 months prior to a breast cancer diagnosis (AUC 0.68; CI 0.565-0.787, p=0.0025). CONCLUSION: Genetically engineered mouse models of cancer may provide a facile discovery tool for identifying autoantibodies useful for human cancer diagnostics.

A Deep Learning Approach to Re-create Raw Full-Field Digital Mammograms for Breast Density and Texture Analysis.

PURPOSE: To develop a computational approach to re-create rarely stored for-processing (raw) digital mammograms from routinely stored for-presentation (processed) mammograms. MATERIALS AND METHODS: In this retrospective study, pairs of raw and processed mammograms collected in 884 women (mean age, 57 years ± 10 [standard deviation]; 3713 mammograms) from October 5, 2017, to August 1, 2018, were examined. Mammograms were split 3088 for training and 625 for testing. A deep learning approach based on a U-Net convolutional network and kernel regression was developed to estimate the raw images. The estimated raw images were compared with the originals by four image error and similarity metrics, breast density calculations, and 29 widely used texture features. RESULTS: In the testing dataset, the estimated raw images had small normalized mean absolute error (0.022 ± 0.015), scaled mean absolute error (0.134 ± 0.078) and mean absolute percentage error (0.115 ± 0.059), and a high structural similarity index (0.986 ± 0.007) for the breast portion compared with the original raw images. The estimated and original raw images had a strong correlation in breast density percentage (Pearson r = 0.946) and a strong agreement in breast density grade (Cohen κ = 0.875). The estimated images had satisfactory correlations with the originals in 23 texture features (Pearson r ≥ 0.503 or Spearman ρ ≥ 0.705) and were well complemented by processed images for the other six features. CONCLUSION: This deep learning approach performed well in re-creating raw mammograms with strong agreement in four image evaluation metrics, breast density, and the majority of 29 widely used texture features.Keywords: Mammography, Breast, Supervised Learning, Convolutional Neural Network (CNN), Deep learning algorithms, Machine Learning AlgorithmsSee also the commentary by Chan in this issue.Supplemental material is available for this article.©RSNA, 2021.

Integral protein microarrays for the identification of lung cancer antigens in sera that induce a humoral immune response.

The identification of biomarkers (both molecules and profiles) in patient sera offers enormous interest for the diagnosis of cancers. In this context, the detection of antibodies to tumor cell autologous antigens possesses great potential. The humoral immune response represents a form of biological amplification of signals that are otherwise weak because of very low concentrations of antigen, especially in the early stages of cancers. Herein we present the use of integral microarrays spotted with tumor-derived proteins to investigate the antibody repertoire in the sera of lung cancer patients and controls. The use of two-dimensional liquid chromatography allowed us to separate proteins from the lung adenocarcinoma cell line A549 into 1760 fractions, which were printed in duplicate, along with various controls, onto nitrocellulose coated slides. The sensitivity and specificity of the microarrays to detect singular antibodies in fluids were first validated through the recognition of fractions containing a lung marker antigen by antibody probing. Twenty fractions were initially selected as highly reactive against the anti-PGP9.5 antibody, and subsequent mass spectrometry analyses confirmed the identity of PGP9.5 protein in four of them. As a result, the importance of neighboring fractions in microarray detection was revealed due to the spreading of proteins during the separation process. Next, the microarrays were individually incubated with 14 serum samples from patients with lung cancer patients, 14 sera from colon cancer patients, and 14 control sera from normal subjects. The reactivity of the selected fractions was analyzed, and the level of immunoglobulin bound to each fraction by each serum sample was quantified. Eight of the 20 fractions offered p values < 0.01 and were recognized by an average of four reacting patients, whereas no serum from normal individuals was positive for those fractions. Protein microarrays from tumor-derived fractions hold the diagnostic potential of uncovering antigens that induce an immune response in patients with certain types of cancers.

HSP90 inhibition enhances cancer immunotherapy by upregulating interferon response genes.

T-cell-based immunotherapies are promising treatments for cancer patients. Although durable responses can be achieved in some patients, many patients fail to respond to these therapies, underscoring the need for improvement with combination therapies. From a screen of 850 bioactive compounds, we identify HSP90 inhibitors as candidates for combination with immunotherapy. We show that inhibition of HSP90 with ganetespib enhances T-cell-mediated killing of patient-derived human melanoma cells by their autologous T cells in vitro and potentiates responses to anti-CTLA4 and anti-PD1 therapy in vivo. Mechanistic studies reveal that HSP90 inhibition results in upregulation of interferon response genes, which are essential for the enhanced killing of ganetespib treated melanoma cells by T cells. Taken together, these findings provide evidence that HSP90 inhibition can potentiate T-cell-mediated anti-tumor immune responses, and rationale to explore the combination of immunotherapy and HSP90 inhibitors.Many patients fail to respond to T cell based immunotherapies. Here, the authors, through a high-throughput screening, identify HSP90 inhibitors as a class of preferred drugs for treatment combination with immunotherapy.

Molecular analysis of cancer using DNA and protein microarrays.

In conclusion, array-based technologies have emerged that contribute to profiling tissues at the genomic, transcriptomic and proteomic levels. Analytical tools are needed to mine the vast amount of data generated. Ultimately the molecular analysis of cancer at a genome and proteome scale will allow better classification of disease and tailored individualized therapy for individual patients.

Distinct sequences on 11q13.5 and 11q23-24 are frequently coamplified with MLL in complexly organized 11q amplicons in AML/MDS patients.

Amplification within chromosome arm 11q involving the mixed-lineage leukemia gene (MLL) locus is a rare but recurrent aberration in acute myeloid leukemia and myelodysplastic syndrome (AML/MDS). We and others have observed that 11q amplifications in most AML/MDS cases have not been restricted to the chromosomal region surrounding the MLL gene. Therefore, we implemented a strategy to characterize comprehensively 11q amplicons in a series of 13 AML/MDS patients with MLL amplification. Analysis of 4 of the 13 cases by restriction landmark genomic scanning in combination with virtual genome scan and by matrix-based comparative genomic hybridization demonstrated that the 11q amplicon in these four cases consisted of at least three discontinuous sequences derived from different regions of the long arm of chromosome 11. We defined a maximally 700-kb sequence around the MLL gene that was amplified in all cases. Apart from the core MLL amplicon, we detected two additional 11q regions that were coamplified. Using fluorescence in situ hybridization (FISH) analysis, we demonstrated that sequences in 11q13.5 and 11q23-24 were amplified in 8 of 13 and 10 of 12 AML/MDS cases, respectively. Both regions harbor a number of potentially oncogenic genes. In all 13 cases, either one or both of these regions were coamplified with the MLL amplicon. Thus, we demonstrated that 11q amplicons in AML/MDS patients display a complex organization and have provided evidence for coamplification of two additional regions on the long arm of chromosome 11 that may harbor candidate target genes.

Amplification and overexpression of the IGF2 regulator PLAG1 in hepatoblastoma.

There is evidence that 8q amplification is associated with poor prognosis in hepatoblastoma. A previous comparative genomic hybridization analysis identified a critical region in chromosomal bands 8q11.2-q13. Using restriction landmark genomic scanning in combination with a virtual genome scan, we showed that this region is delineated by sequences within contig NT_008183 of chromosomal subbands 8q11.22-q11.23. A real-time PCR-based genomic copy number assay of 20 hepatoblastomas revealed gain or amplification in this critical chromosomal region in eight tumors. The expression of four genes and expressed sequence tags (ESTs) within this newly defined region was assayed by real-time reverse transcriptase polymerase chain reaction (RT-PCR) in four tumors with and six tumors without gain or amplification. The PLAG1 oncogene was found to be highly expressed in all but one tumor compared to normal liver tissue. Furthermore, quantitative RT-PCR revealed that the expression level of the developmentally regulated transcription factor PLAG1 was 3-12 times greater in hepatoblastoma tumors and cell lines compared to age-matched normal liver and comparable to the expression in fetal liver tissue. PLAG1 has been shown be a transcriptional activator of IGF2 in other tumor types. Using luciferase reporter assays, we demonstrated that PLAG1 transactivates transcription from the embryonic IGF2 promoter P3, also in hepatoblastoma cell lines. Thus, our results provide evidence that PLAG1 overexpression may be responsible for the frequently observed up-regulation of IGF2 in hepatoblastoma and therefore may be implicated in the molecular pathogenesis of this childhood neoplasia.

Combined restriction landmark genomic scanning and virtual genome scans identify a novel human homeobox gene, ALX3, that is hypermethylated in neuroblastoma.

Restriction landmark genome scanning (RLGS) allows comparative analysis of several thousand DNA fragments in the genome and provides a means to identify CpG islands that are altered in tumor cells as a result of amplification, deletion, or methylation changes. We have developed a novel informatics tool, designated virtual genome scan (VGS), that makes it possible to predict automatically the sequence of fragments in RLGS patterns by matching to the human genome sequence. A combination of RLGS and VGS was utilized to identify changes of chromosome 1-derived fragments in neuroblastoma. A NotI-EcoRV fragment was found to be absent frequently in neuroblastoma cell line RLGS patterns. Sequence prediction by VGS as well as cloning of the fragment showed that it contained a CpG island that is part of the human orthologue of the hamster homeobox gene Alx3. Expression analysis in a panel of human and mouse tissues showed predominant expression of ALX3 in brain tissue. Methylation-sensitive sequence analysis of the promoter region in neuroblastoma cell lines indicated that methylation of specific sequences correlated with repression of the ALX3 gene. Expression was re-induced after treatment with the methylation inhibitor 5-aza-2'-deoxycytidine. Promoter methylation analysis of ALX3 in primary neuroblastoma tumors, using methylation-sensitive polymerase chain reaction, found preferential ALX3 methylation in advanced-stage tumors. The VGS approach we have implemented in combination with RLGS is useful for the identification of genomic CpG island-related methylation changes or deletions in cancer.