Publications by authors named "Beatrice Knudsen"

108 Publications

Imaging of PD-L1 in single cancer cells by SERS-based hyperspectral analysis.

Biomed Opt Express 2020 Nov 8;11(11):6197-6210. Epub 2020 Oct 8.

Department of Biological Engineering, Utah State University, Logan, UT 84322, USA.

We developed a hyperspectral imaging tool based on surface-enhanced Raman spectroscopy (SERS) probes to determine the expression level and visualize the distribution of PD-L1 in individual cells. Electron-microscopic analysis of PD-L1 antibody - gold nanorod conjugates demonstrated binding the cell surface and internalization into endosomal vesicles. Stimulation of cells with IFN-γ or metformin was used to confirm the ability of SERS probes to report treatment-induced changes. The multivariate curve resolution-alternating least squares (MCR-ALS) analysis of spectra provided a greater signal-noise ratio than single peak mapping. However, single peak mapping allowed a systematic subtraction of background and the removal of non-specific binding and endocytic SERS signals. The mean or maximum peak height in the cell or the mean peak height in the area of specific PD-L1 positive pixels was used to estimate the PD-L1 expression levels in single cells. The PD-L1 levels were significantly up-regulated by IFN-γ and inhibited by metformin in human lung cancer cells from the A549 cell line. In conclusion, the method of analyzing hyperspectral SERS imaging data together with systematic and comprehensive removal of non-specific signals allows SERS imaging to be a quantitative tool in the detection of the cancer biomarker, PD-L1.
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http://dx.doi.org/10.1364/BOE.401142DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7687932PMC
November 2020

Publisher Correction to: Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

Diagn Pathol 2020 Sep 24;15(1):116. Epub 2020 Sep 24.

Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA.

An amendment to this paper has been published and can be accessed via the original article.
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http://dx.doi.org/10.1186/s13000-020-01021-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513292PMC
September 2020

Mutant POLQ and POLZ/REV3L DNA polymerases may contribute to the favorable survival of patients with tumors with POLE mutations outside the exonuclease domain.

BMC Med Genet 2020 08 24;21(1):167. Epub 2020 Aug 24.

Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.

Background: Mutations in the exonuclease domain of POLE, a DNA polymerase associated with DNA replication and repair, lead to cancers with ultra-high mutation rates. Most studies focus on intestinal and uterine cancers with POLE mutations. These cancers exhibit a significant immune cell infiltrate and favorable prognosis. We questioned whether loss of function of other DNA polymerases can cooperate to POLE to generate the ultramutator phenotype.

Methods: We used cases and data from 15 cancer types in The Cancer Genome Atlas to investigate mutation frequencies of 14 different DNA polymerases. We tested whether tumor mutation burden, patient outcome (disease-free survival) and immune cell infiltration measured by ESTIMATE can be attributed to mutations in POLQ and POLZ/REV3L.

Results: Thirty six percent of colorectal, stomach and endometrial cancers with POLE mutations carried additional mutations in POLQ (E/Q), POLZ/REV3L (E/Z) or both DNA polymerases (E/Z/Q). The mutation burden in these tumors was significantly greater compared to POLE-only (E) mutant tumors (p < 0.001). In addition, E/Q, E/Z, and E/Q/Z mutant tumors possessed an increased frequency of mutations in the POLE exonuclease domain (p = 0.013). Colorectal, stomach and endometrial E/Q, E/Z, and E/Q/Z mutant tumors within TCGA demonstrated 100% disease-free survival, even if the POLE mutations occurred outside the exonuclease domain (p = 0.003). However, immune scores in these tumors were related to microsatellite instability (MSI) and not POLE mutation status. This suggests that the host immune response may not be the sole mechanism for prolonged disease-free survival of ultramutated tumors in this cohort.

Conclusion: Results in this study demonstrate that mutations in POLQ and REV3L in POLE mutant tumors should undergo further investigation to determine whether POLQ and REV3L mutations contribute to the ultramutator phenotype and favorable outcome of patients with POLE mutant tumors.
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http://dx.doi.org/10.1186/s12881-020-01089-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446057PMC
August 2020

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

Diagn Pathol 2020 Jul 28;15(1):100. Epub 2020 Jul 28.

Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA.

Background: Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel.

Methods: Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and ensemble methods that employ both ColorAE and U-Net, collectively referred to as (3) ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor).

Results: We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect 6 different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net into ensemble methods outperform using either ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also present a use case, wherein we apply the ColorAE:U-Net ensemble method across 3 mIHC WSIs and use the predictions to quantify all stained cell populations and perform nearest neighbor spatial analysis. Thus, we provide proof of concept that these methods can be employed to quantitatively describe the spatial distribution immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.
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http://dx.doi.org/10.1186/s13000-020-01003-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385962PMC
July 2020

The Movember Prostate Cancer Landscape Analysis: an assessment of unmet research needs.

Nat Rev Urol 2020 Sep 22;17(9):499-512. Epub 2020 Jul 22.

Movember, Melbourne, Victoria, Australia.

Prostate cancer is a heterogeneous cancer with widely varying levels of morbidity and mortality. Approaches to prostate cancer screening, diagnosis, surveillance, treatment and management differ around the world. To identify the highest priority research needs across the prostate cancer biomedical research domain, Movember conducted a landscape analysis with the aim of maximizing the effect of future research investment through global collaborative efforts and partnerships. A global Landscape Analysis Committee (LAC) was established to act as an independent group of experts across urology, medical oncology, radiation oncology, radiology, pathology, translational research, health economics and patient advocacy. Men with prostate cancer and thought leaders from a variety of disciplines provided a range of key insights through a range of interviews. Insights were prioritized against predetermined criteria to understand the areas of greatest unmet need. From these efforts, 17 research needs in prostate cancer were agreed on and prioritized, and 3 received the maximum prioritization score by the LAC: first, to establish more sensitive and specific tests to improve disease screening and diagnosis; second, to develop indicators to better stratify low-risk prostate cancer for determining which men should go on active surveillance; and third, to integrate companion diagnostics into randomized clinical trials to enable prediction of treatment response. On the basis of the findings from the landscape analysis, Movember will now have an increased focus on addressing the specific research needs that have been identified, with particular investment in research efforts that reduce disease progression and lead to improved therapies for advanced prostate cancer.
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http://dx.doi.org/10.1038/s41585-020-0349-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462750PMC
September 2020

The intraprostatic immune environment after stereotactic body radiotherapy is dominated by myeloid cells.

Prostate Cancer Prostatic Dis 2020 Jul 9. Epub 2020 Jul 9.

Radiation Oncology at UCLA, Los Angeles, CA, USA.

Background: Hundreds of ongoing clinical trials combine radiation therapy, mostly delivered as stereotactic body radiotherapy (SBRT), with immune checkpoint blockade. However, our understanding of the effect of radiotherapy on the intratumoral immune balance is inadequate, hindering the optimal design of trials that combine radiation therapy with immunotherapy. Our objective was to characterize the intratumoral immune balance of the malignant prostate after SBRT in patients.

Methods: Sixteen patients with high-risk, non-metastatic prostate cancer at comparable Gleason Grade disease underwent radical prostatectomy with (n = 9) or without (n = 7) neoadjuvant SBRT delivered in three fractions of 8 Gy over 5 days completed 2 weeks before surgery. Freshly resected prostate specimens were processed to obtain single-cell suspensions, and immune-phenotyped for major lymphoid and myeloid cell subsets by staining with two separate 14-antibody panels and multicolor flow cytometry analysis.

Results: Malignant prostates 2 weeks after SBRT had an immune infiltrate dominated by myeloid cells, whereas malignant prostates without preoperative treatment were more lymphoid-biased (myeloid CD45 cells 48.4 ± 19.7% vs. 25.4 ± 7.0%; adjusted p-value = 0.11; and CD45 lymphocytes 51.6 ± 19.7% vs. 74.5 ± 7.0%; p = 0.11; CD3 T cells 35.2 ± 23.8% vs. 60.9 ± 9.7%; p = 0.12; mean ± SD).

Conclusion: SBRT drives a significant lymphoid to myeloid shift in the prostate-tumor immune infiltrate. This may be of interest when combining SBRT with immunotherapies, particularly in prostate cancer.
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http://dx.doi.org/10.1038/s41391-020-0249-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794088PMC
July 2020

Phase 1 Trial of Stereotactic Body Radiation Therapy Neoadjuvant to Radical Prostatectomy for Patients With High-Risk Prostate Cancer.

Int J Radiat Oncol Biol Phys 2020 Nov 17;108(4):930-935. Epub 2020 Jun 17.

Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California; Department of Urology, University of California Los Angeles, Los Angeles, California; Radiation Therapy Service, VA Greater Los Angeles Healthcare System, Los Angeles, California. Electronic address:

Purpose: This study aimed to evaluate the feasibility and safety of prostate stereotactic body radiation therapy (SBRT) neoadjuvant to radical prostatectomy (RP) in a phase 1 trial. The primary endpoint was treatment completion rate without severe acute surgical complications. Secondary endpoints included patient-reported quality of life and physician-reported toxicities.

Methods And Materials: Patients with nonmetastatic high-risk or locally advanced prostate cancer received 24 Gy in 3 fractions to the prostate and seminal vesicles over 5 days, completed 2 weeks before RP. Patients with pN1 disease were treated after multidisciplinary discussion and shared decision making. Patient-reported quality of life (International Prostate Symptom Score and Expanded Prostate Cancer Index Composite 26-item version questionnaires) and physician-reported toxicity (Common Terminology Criteria for Adverse Events, version 4.03) were assessed before SBRT, immediately before surgery, and at 3-month intervals for 1 year.

Results: Twelve patients were enrolled, and 11 completed treatment (1 patient had advanced disease on prostate-specific membrane antigen positron emission tomography after enrollment but before treatment). There were no significant surgical complications. After RP, 2 patients underwent additional radiation therapy to nodes with androgen suppression for pN1 disease. Median follow-up after completion of treatment was 20.1 months, with 9 of 11 patients having a follow-up period of >12 months. Two patients had biochemical recurrence (prostate-specific antigen ≥0.05) within the first 12 months, with an additional 2 patients found to have biochemical recurrence after the 12-month period. The highest Common Terminology Criteria for Adverse Events genitourinary grades were 0, 1, 2, and 3 (n = 1, 4, 4, and 2, respectively), and the highest gastrointestinal grades were 0, 1, and 2 (n = 9, 1, and 1, respectively). At 12 months, incontinence was the only grade ≥2 toxicity. One and 2 of 9 patients had grade 2 and 3 incontinence, respectively. On the Expanded Prostate Cancer Index Composite (26-item version), the mean/median changes in scores from baseline to 12 months were -32.8/-31.1 for urinary incontinence, -1.6/-6.2 for urinary irritative/obstructive, -2.1/0 for bowel, -34.4/-37.5 for sexual function, and -10.6/-2.5 for hormonal. The mean/median change in International Prostate Symptom Score from baseline to 12 months was 0.5/0.5.

Conclusions: RP after neoadjuvant SBRT appears to be feasible and safe at the dose tested. The severity of urinary incontinence may be higher than RP alone.
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http://dx.doi.org/10.1016/j.ijrobp.2020.06.010DOI Listing
November 2020

Chromosomal instability in untreated primary prostate cancer as an indicator of metastatic potential.

BMC Cancer 2020 May 7;20(1):398. Epub 2020 May 7.

Department of Urology, David Geffen School of Medicine at UCLA, Box 951738, 10833 Le Conte Ave 66-188 CHS UCLA, Los Angeles, CA, 90095, USA.

Background: Metastatic prostate cancer (PC) is highly lethal. The ability to identify primary tumors capable of dissemination is an unmet need in the quest to understand lethal biology and improve patient outcomes. Previous studies have linked chromosomal instability (CIN), which generates aneuploidy following chromosomal missegregation during mitosis, to PC progression. Evidence of CIN includes broad copy number alterations (CNAs) spanning > 300 base pairs of DNA, which may also be measured via RNA expression signatures associated with CNA frequency. Signatures of CIN in metastatic PC, however, have not been interrogated or well defined. We examined a published 70-gene CIN signature (CIN70) in untreated and castration-resistant prostate cancer (CRPC) cohorts from The Cancer Genome Atlas (TCGA) and previously published reports. We also performed transcriptome and CNA analysis in a unique cohort of untreated primary tumors collected from diagnostic prostate needle biopsies (PNBX) of localized (M0) and metastatic (M1) cases to determine if CIN was linked to clinical stage and outcome.

Methods: PNBX were collected from 99 patients treated in the VA Greater Los Angeles (GLA-VA) Healthcare System between 2000 and 2016. Total RNA was extracted from high-grade cancer areas in PNBX cores, followed by RNA sequencing and/or copy number analysis using OncoScan. Multivariate logistic regression analyses permitted calculation of odds ratios for CIN status (high versus low) in an expanded GLA-VA PNBX cohort (n = 121).

Results: The CIN70 signature was significantly enriched in primary tumors and CRPC metastases from M1 PC cases. An intersection of gene signatures comprised of differentially expressed genes (DEGs) generated through comparison of M1 versus M0 PNBX and primary CRPC tumors versus metastases revealed a 157-gene "metastasis" signature that was further distilled to 7-genes (PC-CIN) regulating centrosomes, chromosomal segregation, and mitotic spindle assembly. High PC-CIN scores correlated with CRPC, PC-death and all-cause mortality in the expanded GLA-VA PNBX cohort. Interestingly, approximately 1/3 of M1 PNBX cases exhibited low CIN, illuminating differential pathways of lethal PC progression.

Conclusions: Measuring CIN in PNBX by transcriptome profiling is feasible, and the PC-CIN signature may identify patients with a high risk of lethal progression at the time of diagnosis.
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http://dx.doi.org/10.1186/s12885-020-06817-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204307PMC
May 2020

Centrosome loss results in an unstable genome and malignant prostate tumors.

Oncogene 2020 01 2;39(2):399-413. Epub 2019 Sep 2.

Department of Cellular and Molecular Medicine, University of Arizona Cancer Center, University of Arizona, Tucson, AZ, 85724, USA.

Localized, nonindolent prostate cancer (PCa) is characterized by large-scale genomic rearrangements, aneuploidy, chromothripsis, and other forms of chromosomal instability (CIN), yet how this occurs remains unclear. A well-established mechanism of CIN is the overproduction of centrosomes, which promotes tumorigenesis in various mouse models. Therefore, we developed a single-cell assay for quantifying centrosomes in human prostate tissue. Surprisingly, centrosome loss-which has not been described in human cancer-was associated with PCa progression. By chemically or genetically inducing centrosome loss in nontumorigenic prostate epithelial cells, mitotic errors ensued, producing aneuploid, and multinucleated cells. Strikingly, transient or chronic centrosome loss transformed prostate epithelial cells, which produced highly proliferative and poorly differentiated malignant tumors in mice. Our findings suggest that centrosome loss could create a cellular crisis with oncogenic potential in prostate epithelial cells.
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http://dx.doi.org/10.1038/s41388-019-0995-zDOI Listing
January 2020

A Circulating Tumor Cell-RNA Assay for Assessment of Androgen Receptor Signaling Inhibitor Sensitivity in Metastatic Castration-Resistant Prostate Cancer.

Theranostics 2019 13;9(10):2812-2826. Epub 2019 Apr 13.

Urologic Oncology Program and Uro-Oncology Research Laboratories, Cedars-Sinai Medical Center, Los Angeles, California, USA.

: Our objective was to develop a circulating tumor cell (CTC)-RNA assay for characterizing clinically relevant RNA signatures for the assessment of androgen receptor signaling inhibitor (ARSI) sensitivity in metastatic castration-resistant prostate cancer (mCRPC) patients. : We developed the NanoVelcro CTC-RNA assay by combining the Thermoresponsive (TR)-NanoVelcro CTC purification system with the NanoString nCounter platform for cellular purification and RNA analysis. Based on the well-validated, tissue-based Prostate Cancer Classification System (PCS), we focus on the most aggressive and ARSI-resistant PCS subtype, i.e., PCS1, for CTC analysis. We applied a rigorous bioinformatic process to develop the CTC-PCS1 panel that consists of prostate cancer (PCa) CTC-specific RNA signature with minimal expression in background white blood cells (WBCs). We validated the NanoVelcro CTC-RNA assay and the CTC-PCS1 panel with well-characterized PCa cell lines to demonstrate the sensitivity and dynamic range of the assay, as well as the specificity of the PCS1 Z score (the likelihood estimate of the PCS1 subtype) for identifying PCS1 subtype and ARSI resistance. We then selected 31 blood samples from 23 PCa patients receiving ARSIs to test in our assay. The PCS1 Z scores of each sample were computed and compared with ARSI treatment sensitivity. : The validation studies using PCa cell line samples showed that the NanoVelcro CTC-RNA assay can detect the RNA transcripts in the CTC-PCS1 panel with high sensitivity and linearity in the dynamic range of 5-100 cells. We also showed that the genes in CTC-PCS1 panel are highly expressed in PCa cell lines and lowly expressed in background WBCs. Using the artificial CTC samples simulating the blood sample conditions, we further demonstrated that the CTC-PCS1 panel is highly specific in identifying PCS1-like samples, and the high PCS1 Z score is associated with ARSI resistance samples. In patient bloods, ARSI-resistant samples (ARSI-R, n=14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n=17) (Rank-sum test, P=0.003). In the analysis of 8 patients who were initially sensitive to ARSI (ARSI-S) and later developed resistance (ARSI-R), we found that the PCS1 Z score increased from the time of ARSI-S to the time of ARSI-R (Pairwise T-test, P=0.016). : Using our new methodology, we developed a first-in-class CTC-RNA assay and demonstrated the feasibility of transforming clinically-relevant tissue-based RNA profiling such as PCS into CTC tests. This approach allows for detecting RNA expression relevant to clinical drug resistance in a non-invasive fashion, which can facilitate patient-specific treatment selection and early detection of drug resistance, a goal in precision oncology.
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http://dx.doi.org/10.7150/thno.34485DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6568173PMC
May 2020

Clinical protein science in translational medicine targeting malignant melanoma.

Cell Biol Toxicol 2019 08 21;35(4):293-332. Epub 2019 Mar 21.

Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden.

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.
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http://dx.doi.org/10.1007/s10565-019-09468-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6757020PMC
August 2019

Integrin α6β4E variant is associated with actin and CD9 structures and modifies the biophysical properties of cell-cell and cell-extracellular matrix interactions.

Mol Biol Cell 2019 03 13;30(7):838-850. Epub 2019 Mar 13.

Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ 85724.

Integrin α6β4 is an essential, dynamic adhesion receptor for laminin 332 found on epithelial cells, required for formation of strong cell-extracellular matrix (ECM) adhesion and induced migration, and coordinated by regions of the β4C cytoplasmic domain. β4E, a unique splice variant of β4 expressed in normal tissue, contains a cytoplasmic domain of 231 amino acids with a unique sequence of 114 amino acids instead of β4C's canonical 1089 amino acids. We determined the distribution of α6β4E within normal human glandular epithelium and its regulation and effect on cellular biophysical properties. Canonical α6β4C expressed in all basal cells, as expected, while α6β4E expressed within a subset of luminal cells. α6β4E expression was induced by three-dimensional culture conditions, activated Src, was reversible, and was stabilized by bortezomib, a proteasome inhibitor. α6β4C expressed in all cells during induced migration, whereas α6β4E was restricted to a subset of cells with increased kinetics of cell-cell and cell-ECM resistance properties. Interestingly, α6β4E presented in "ringlike" patterns measuring ∼1.75 × 0.72 microns and containing actin and CD9 at cell-ECM locations. In contrast, α6β4C expressed only within hemidesmosome-like structures containing BP180. Integrin α6β4E is an inducible adhesion isoform in normal epithelial cells that can alter biophysical properties of cell-cell and cell-ECM interactions.
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http://dx.doi.org/10.1091/mbc.E18-10-0652DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589785PMC
March 2019

Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides.

Sci Rep 2019 02 6;9(1):1483. Epub 2019 Feb 6.

Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.

During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.
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http://dx.doi.org/10.1038/s41598-018-37638-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6365499PMC
February 2019

A method of quantifying centrosomes at the single-cell level in human normal and cancer tissue.

Mol Biol Cell 2019 03 30;30(7):811-819. Epub 2019 Jan 30.

Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ 85724.

Centrosome abnormalities are emerging hallmarks of cancer. The overproduction of centrosomes (known as centrosome amplification) has been reported in a variety of cancers and is currently being explored as a promising target for therapy. However, to understand different types of centrosome abnormalities and their impact on centrosome function during tumor progression, as well as to identify tumor subtypes that would respond to the targeting of a centrosome abnormality, a reliable method for accurately quantifying centrosomes in human tissue samples is needed. Here, we established a method of quantifying centrosomes at a single-cell level in different types of human tissue samples. We tested multiple anti-centriole and pericentriolar-material antibodies to identify bona fide centrosomes and multiplexed these with cell border markers to identify individual cells within the tissue. High-resolution microscopy was used to generate multiple Z-section images, allowing us to acquire whole cell volumes in which to scan for centrosomes. The normal cells within the tissue serve as internal positive controls. Our method provides a simple, accurate way to distinguish alterations in centrosome numbers at the level of single cells.
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http://dx.doi.org/10.1091/mbc.E18-10-0651DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6589791PMC
March 2019

Spatial Mapping of Myeloid Cells and Macrophages by Multiplexed Tissue Staining.

Front Immunol 2018 14;9:2925. Epub 2018 Dec 14.

Departments of Biomedical Sciences, Pathology, Surgery and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States.

An array of phenotypically diverse myeloid cells and macrophages (MC&M) resides in the tumor microenvironment, requiring multiplexed detection systems for visualization. Here we report an automated, multiplexed staining approach, named PLEXODY, that consists of five MC&M-related fluorescently-tagged antibodies (anti - CD68, - CD163, - CD206, - CD11b, and - CD11c), and three chromogenic antibodies, reactive with high- and low-molecular weight cytokeratins and CD3, highlighting tumor regions, benign glands and T cells. The staining prototype and image analysis methods which include a pixel/area-based quantification were developed using tissues from inflamed colon and tonsil and revealed a unique tissue-specific composition of 14 MC&M-associated pixel classes. As a proof-of-principle, PLEXODY was applied to three cases of pancreatic, prostate and renal cancers. Across digital images from these cancer types we observed 10 MC&M-associated pixel classes at frequencies greater than 3%. Cases revealed higher frequencies of single positive compared to multi-color pixels and a high abundance of CD68+/CD163+ and CD68+/CD163+/CD206+ pixels. Significantly more CD68+ and CD163+ vs. CD11b+ and CD11c+ pixels were in direct contact with tumor cells and T cells. While the greatest percentage (~70%) of CD68+ and CD163+ pixels was 0-20 microns away from tumor and T cell borders, CD11b+ and CD11c+ pixels were detected up to 240 microns away from tumor/T cell masks. Together, these data demonstrate significant differences in densities and spatial organization of MC&M-associated pixel classes, but surprising similarities between the three cancer types.
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http://dx.doi.org/10.3389/fimmu.2018.02925DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302234PMC
October 2019

Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance.

Prostate Cancer Prostatic Dis 2019 09 12;22(3):399-405. Epub 2018 Dec 12.

University of Calgary, Calgary, AB, Canada.

Abstact: BACKGROUND: Many men diagnosed with prostate cancer are active surveillance (AS) candidates. However, AS may be associated with increased risk of disease progression and metastasis due to delayed therapy. Genomic classifiers, e.g., Decipher, may allow better risk-stratify newly diagnosed prostate cancers for AS.

Methods: Decipher was initially assessed in a prospective cohort of prostatectomies to explore the correlation with clinically meaningful biologic characteristics and then assessed in diagnostic biopsies from a retrospective multicenter cohort of 266 men with National Comprehensive Cancer Network (NCCN) very low/low and favorable-intermediate risk prostate cancer. Decipher and Cancer of the Prostate Risk Assessment (CAPRA) were compared as predictors of adverse pathology (AP) for which there is universal agreement that patients with long life-expectancy are not suitable candidates for AS (primary pattern 4 or 5, advanced local stage [pT3b or greater] or lymph node involvement).

Results: Decipher from prostatectomies was significantly associated with adverse pathologic features (p-values < 0.001). Decipher from the 266 diagnostic biopsies (64.7% NCCN-very-low/low and 35.3% favorable-intermediate) was an independent predictor of AP (odds ratio 1.29 per 10% increase, 95% confidence interval [CI] 1.03-1.61, p-value 0.025) when adjusting for CAPRA. CAPRA area under curve (AUC) was 0.57, (95% CI 0.47-0.68). Adding Decipher to CAPRA increased the AUC to 0.65 (95% CI 0.58-0.70). NPV, which determines the degree of confidence in the absence of AP for patients, was 91% (95% CI 87-94%) and 96% (95% CI 90-99%) for Decipher thresholds of 0.45 and 0.2, respectively. Using a threshold of 0.2, Decipher was a significant predictor of AP when adjusting for CAPRA (p-value 0.016).

Conclusion: Decipher can be applied to prostate biopsies from NCCN-very-low/low and favorable-intermediate risk patients to predict absence of adverse pathologic features. These patients are predicted to be good candidates for active surveillance.
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http://dx.doi.org/10.1038/s41391-018-0101-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760567PMC
September 2019

Procurement and Storage of Pleural and Peritoneal Fluids for Biobanking.

Methods Mol Biol 2019 ;1897:125-133

Department of Pathology and Laboratory Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA.

There is limited information regarding the biobanking of pleural and peritoneal fluids that might supplement storage of pulmonary and thoracic tissue biospecimens. Such fluids are sometimes collected for clinical analyses and may have uses that obviate or supplement tissue samples. There has been a growing interest in using liquid biopsies as they are less invasive and may be amenable to analyses that guide targeted therapies. Integrating cytology and biobanking approaches, we describe techniques that may be used for collecting and banking pleural and peritoneal fluids.
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http://dx.doi.org/10.1007/978-1-4939-8935-5_13DOI Listing
June 2019

ONECUT2 is a targetable master regulator of lethal prostate cancer that suppresses the androgen axis.

Nat Med 2018 12 26;24(12):1887-1898. Epub 2018 Nov 26.

Division of Cancer Biology and Therapeutics, Departments of Surgery & Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Treatment of prostate cancer (PC) by androgen suppression promotes the emergence of aggressive variants that are androgen receptor (AR) independent. Here we identify the transcription factor ONECUT2 (OC2) as a master regulator of AR networks in metastatic castration-resistant prostate cancer (mCRPC). OC2 acts as a survival factor in mCRPC models, suppresses the AR transcriptional program by direct regulation of AR target genes and the AR licensing factor FOXA1, and activates genes associated with neural differentiation and progression to lethal disease. OC2 appears active in a substantial subset of human prostate adenocarcinoma and neuroendocrine tumors. Inhibition of OC2 by a newly identified small molecule suppresses metastasis in mice. These findings suggest that OC2 displaces AR-dependent growth and survival mechanisms in many cases where AR remains expressed, but where its activity is bypassed. OC2 is also a potential drug target in the metastatic phase of aggressive PC.
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http://dx.doi.org/10.1038/s41591-018-0241-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614557PMC
December 2018

PARP-1 regulates DNA repair factor availability.

EMBO Mol Med 2018 12;10(12)

Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA, USA.

PARP-1 holds major functions on chromatin, DNA damage repair and transcriptional regulation, both of which are relevant in the context of cancer. Here, unbiased transcriptional profiling revealed the downstream transcriptional profile of PARP-1 enzymatic activity. Further investigation of the PARP-1-regulated transcriptome and secondary strategies for assessing PARP-1 activity in patient tissues revealed that PARP-1 activity was unexpectedly enriched as a function of disease progression and was associated with poor outcome independent of DNA double-strand breaks, suggesting that enhanced PARP-1 activity may promote aggressive phenotypes. Mechanistic investigation revealed that active PARP-1 served to enhance E2F1 transcription factor activity, and specifically promoted E2F1-mediated induction of DNA repair factors involved in homologous recombination (HR). Conversely, PARP-1 inhibition reduced HR factor availability and thus acted to induce or enhance "BRCA-ness". These observations bring new understanding of PARP-1 function in cancer and have significant ramifications on predicting PARP-1 inhibitor function in the clinical setting.
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http://dx.doi.org/10.15252/emmm.201708816DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284389PMC
December 2018

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images.

IEEE Trans Med Imaging 2019 04 12;38(4):945-954. Epub 2018 Oct 12.

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.
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http://dx.doi.org/10.1109/TMI.2018.2875868DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497079PMC
April 2019

Effect of Preanalytic Variables on an Automated PTEN Immunohistochemistry Assay for Prostate Cancer.

Arch Pathol Lab Med 2019 03 8;143(3):338-348. Epub 2018 Oct 8.

From the Departments of Pathology (Drs Guedes, Morais, Fedor, Hicks, Gurel, De Marzo, and Lotan), Oncology (Drs Trock, De Marzo, and Lotan), and Urology (Drs Trock and De Marzo), Johns Hopkins University School of Medicine, Baltimore, Maryland; the Department of Pathology, New York University School of Medicine, New York, New York (Drs Melamed and Lee); the Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York (Drs Gopalan and Fine); the Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, California (Dr Knudsen); the Department of Pathology, University of Washington, Seattle (Dr True); and Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center and Weill Cornell Medical College, New York, New York (Dr Scher).

Context.—: Phosphatase and tensin homolog (PTEN) is a promising prognostic and potentially predictive biomarker in prostate cancer.

Objective.—: To assess the effects of preanalytic variables on an analytically validated and fully automated PTEN immunohistochemistry assay.

Design.—: PTEN immunohistochemistry was performed on Ventana immunostaining systems. In benign prostate tissues, immunostaining intensity across variable conditions was assessed by digital image analysis. In prostate tumor tissues, immunostaining was scored visually.

Results.—: Delay of fixation for 4 hours or longer at room temperature or 48 hours or longer at 4°C and duration of formalin fixation did not significantly alter immunostaining intensity. Intensity of staining was highest in 10% formalin compared with other fixatives. Tumor tissues with PTEN loss processed using protocols from 11 academic institutions were all evaluable and scored identically. PTEN immunostaining of needle biopsies where tissue blocks had been stored for less than 10 years was more frequently scored as nonevaluable compared with blocks that had been stored for 10 years or longer. This effect was less evident for radical prostatectomy specimens, where low rates of nonevaluable staining were seen for 23 years or more of storage. Storage of unstained slides for 5 years at room temperature prior to immunostaining resulted in equivalent scoring compared with freshly cut slides. Machine-to-machine variability assessed across 3 Ventana platforms and 2 institutions was negligible in 12 tumors, and platform-to-platform variability was also minor comparing Ventana and Leica instruments across 77 tumors (κ = 0.926).

Conclusions.—: Automated PTEN immunostaining is robust to most preanalytic variables in the prostate and may be performed on prostate tumor tissues subjected to a wide range of preanalytic conditions. These data may help guide assay development if PTEN becomes a key predictive biomarker.
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http://dx.doi.org/10.5858/arpa.2018-0068-OADOI Listing
March 2019

An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies.

Comput Med Imaging Graph 2018 11 3;69:125-133. Epub 2018 Sep 3.

Department of Bioengineering, University of California, Los Angeles, CA, USA; Computational Integrated Diagnostics, Departments of Radiological Sciences and Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA. Electronic address:

Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create. One way to address this problem is to leverage external weakly labeled datasets to augment models trained on the limited data. In this paper, we developed an expectation maximization-based approach constrained by an approximated prior distribution in order to extract useful representations from a large number of weakly labeled images generated from low-magnification annotations. This method was utilized to improve the performance of a model trained on a limited fully annotated dataset. Our semi-supervised approach trained with 135 fully annotated and 1800 weakly annotated tiles achieved a mean Jaccard Index of 49.5% on an independent test set, which was 14% higher than the initial model trained only on the fully annotated dataset.
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http://dx.doi.org/10.1016/j.compmedimag.2018.08.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173982PMC
November 2018

Emerin Deregulation Links Nuclear Shape Instability to Metastatic Potential.

Cancer Res 2018 11 28;78(21):6086-6097. Epub 2018 Aug 28.

Division of Cancer Biology and Therapeutics, Department of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.

Abnormalities in nuclear shape are a well-known feature of cancer, but their contribution to malignant progression remains poorly understood. Here, we show that depletion of the cytoskeletal regulator, Diaphanous-related formin 3 (DIAPH3), or the nuclear membrane-associated proteins, lamin A/C, in prostate and breast cancer cells, induces nuclear shape instability, with a corresponding gain in malignant properties, including secretion of extracellular vesicles that contain genomic material. This transformation is characterized by a reduction and/or mislocalization of the inner nuclear membrane protein, emerin. Consistent with this, depletion of emerin evokes nuclear shape instability and promotes metastasis. By visualizing emerin localization, evidence for nuclear shape instability was observed in cultured tumor cells, in experimental models of prostate cancer, in human prostate cancer tissues, and in circulating tumor cells from patients with metastatic disease. Quantitation of emerin mislocalization discriminated cancer from benign tissue and correlated with disease progression in a prostate cancer cohort. Taken together, these results identify emerin as a mediator of nuclear shape stability in cancer and show that destabilization of emerin can promote metastasis. This study identifies a novel mechanism integrating the control of nuclear structure with the metastatic phenotype, and our inclusion of two types of human specimens (cancer tissues and circulating tumor cells) demonstrates direct relevance to human cancer. http://cancerres.aacrjournals.org/content/canres/78/21/6086/F1.large.jpg .
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http://dx.doi.org/10.1158/0008-5472.CAN-18-0608DOI Listing
November 2018

The actin cytoskeletal architecture of estrogen receptor positive breast cancer cells suppresses invasion.

Nat Commun 2018 07 30;9(1):2980. Epub 2018 Jul 30.

Department of Cellular and Molecular Medicine, University of Arizona Cancer Center, University of Arizona, Tucson, 85724, AZ, USA.

Estrogen promotes growth of estrogen receptor-positive (ER+) breast tumors. However, epidemiological studies examining the prognostic characteristics of breast cancer in postmenopausal women receiving hormone replacement therapy reveal a significant decrease in tumor dissemination, suggesting that estrogen has potential protective effects against cancer cell invasion. Here, we show that estrogen suppresses invasion of ER+ breast cancer cells by increasing transcription of the Ena/VASP protein, EVL, which promotes the generation of suppressive cortical actin bundles that inhibit motility dynamics, and is crucial for the ER-mediated suppression of invasion in vitro and in vivo. Interestingly, despite its benefits in suppressing tumor growth, anti-estrogenic endocrine therapy decreases EVL expression and increases local invasion in patients. Our results highlight the dichotomous effects of estrogen on tumor progression and suggest that, in contrast to its established role in promoting growth of ER+ tumors, estrogen has a significant role in suppressing invasion through actin cytoskeletal remodeling.
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http://dx.doi.org/10.1038/s41467-018-05367-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065369PMC
July 2018

A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors.

Nat Genet 2018 07 18;50(7):979-989. Epub 2018 Jun 18.

Department of Systems Biology, Columbia University, New York, NY, USA.

We introduce and validate a new precision oncology framework for the systematic prioritization of drugs targeting mechanistic tumor dependencies in individual patients. Compounds are prioritized on the basis of their ability to invert the concerted activity of master regulator proteins that mechanistically regulate tumor cell state, as assessed from systematic drug perturbation assays. We validated the approach on a cohort of 212 gastroenteropancreatic neuroendocrine tumors (GEP-NETs), a rare malignancy originating in the pancreas and gastrointestinal tract. The analysis identified several master regulator proteins, including key regulators of neuroendocrine lineage progenitor state and immunoevasion, whose role as critical tumor dependencies was experimentally confirmed. Transcriptome analysis of GEP-NET-derived cells, perturbed with a library of 107 compounds, identified the HDAC class I inhibitor entinostat as a potent inhibitor of master regulator activity for 42% of metastatic GEP-NET patients, abrogating tumor growth in vivo. This approach may thus complement current efforts in precision oncology.
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http://dx.doi.org/10.1038/s41588-018-0138-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421579PMC
July 2018

A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.

AMIA Annu Symp Proc 2017 16;2017:1140-1148. Epub 2018 Apr 16.

Department of Bioengineering, University of California, Los Angeles, CA, USA.

Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. Much research has been done in classifying small homogeneous cancer regions within histological images. However, semi-supervised methods published to date depend on pre-selected regions and cannot be easily extended to an image of heterogeneous tissue composition. In this paper, we propose a multi-scale U-Net model to classify images at the pixel-level using 224 histological image tiles from radical prostatectomies of 20 patients. Our model was evaluated by a patient-based 10-fold cross validation, and achieved a mean Jaccard index of 65.8% across 4 classes (stroma, Gleason 3, Gleason 4 and benign glands), and 75.5% for 3 classes (stroma, benign glands, prostate cancer), outperforming other methods.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977596PMC
April 2019

A Non-integrating Lentiviral Approach Overcomes Cas9-Induced Immune Rejection to Establish an Immunocompetent Metastatic Renal Cancer Model.

Mol Ther Methods Clin Dev 2018 Jun 23;9:203-210. Epub 2018 Feb 23.

Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.

The CRISPR-based technology has revolutionized genome editing in recent years. This technique allows for gene knockout and evaluation of function in cell lines in a manner that is far easier and more accessible than anything previously available. Unfortunately, the ability to extend these studies to syngeneic murine cell line implantation is limited by an immune response against cells transduced to stably express Cas9. In this study, we demonstrate that a non-integrating lentiviral vector approach can overcome this immune rejection and allow for the growth of transduced cells in an immunocompetent host. This technique enables the establishment of a von Hippel-Lindau () gene knockout RENCA cell line in BALB/c mice, generating an improved model of immunocompetent, metastatic renal cell carcinoma (RCC).
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http://dx.doi.org/10.1016/j.omtm.2018.02.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948229PMC
June 2018

Novel Regulation of Integrin Trafficking by Rab11-FIP5 in Aggressive Prostate Cancer.

Mol Cancer Res 2018 08 14;16(8):1319-1331. Epub 2018 May 14.

The University of Arizona Cancer Center, University of Arizona, Tucson, Arizona.

The laminin-binding integrins, α3β1 and α6β1, are needed for tumor metastasis and their surface expression is regulated by endocytic recycling. β1 integrins share the Rab11 recycling machinery, but the trafficking of α3β1 and α6β1 are distinct by an unknown mechanism. Using a mouse PDX tumor model containing human metastatic prostate cancer, Rab11 family interacting protein 5 (Rab11-FIP5) was identified as a lead candidate for α6β1 trafficking. Rab11-FIP5 and its membrane-binding domain were required for α6β1 recycling, without affecting the other laminin-binding integrin (i.e., α3β1) or unrelated membrane receptors like CD44, transferrin receptor, or E-cadherin. Depletion of Rab11-FIP5 resulted in the intracellular accumulation of α6β1 in the Rab11 recycling compartment, loss of cell migration on laminin, and an unexpected loss of α6β1 recycling in cell-cell locations. Taken together, these data demonstrate that α6β1 is distinct from α3β1 via Rab11-FIP5 recycling and recycles in an unexpected cell-cell location. Rab11-FIP5-dependent α6β1 integrin recycling may be selectively targeted to limit migration of prostate cancer cells into laminin-rich tissues. .
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http://dx.doi.org/10.1158/1541-7786.MCR-17-0589DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369592PMC
August 2018

Regulation of inside-out β1-integrin activation by CDCP1.

Oncogene 2018 05 7;37(21):2817-2836. Epub 2018 Mar 7.

Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.

Tumor metastasis depends on the dynamic regulation of cell adhesion through β1-integrin. The Cub-Domain Containing Protein-1, CDCP1, is a transmembrane glycoprotein which regulates cell adhesion. Overexpression and loss of CDCP1 have been observed in the same cancer types to promote metastatic progression. Here, we demonstrate reduced CDCP1 expression in high-grade, primary prostate cancers, circulating tumor cells and tumor metastases of patients with castrate-resistant prostate cancer. CDCP1 is expressed in epithelial and not mesenchymal cells, and its cell surface and mRNA expression declines upon stimulation with TGFβ1 and epithelial-to-mesenchymal transition. Silencing of CDCP1 in DU145 and PC3 cells resulted in 3.4-fold higher proliferation of non-adherent cells and 4.4-fold greater anchorage independent growth. CDCP1-silenced tumors grew in 100% of mice, compared to 30% growth of CDCP1-expressing tumors. After CDCP1 silencing, cell adhesion and migration diminished 2.1-fold, caused by loss of inside-out activation of β1-integrin. We determined that the loss of CDCP1 reduces CDK5 kinase activity due to the phosphorylation of its regulatory subunit, CDK5R1/p35, by c-SRC on Y234. This generates a binding site for the C2 domain of PKCδ, which in turn phosphorylates CDK5 on T77. The resulting dissociation of the CDK5R1/CDK5 complex abolishes the activity of CDK5. Mutations of CDK5-T77 and CDK5R1-Y234 phosphorylation sites re-establish the CDK5/CDKR1 complex and the inside-out activity of β1-integrin. Altogether, we discovered a new mechanism of regulation of CDK5 through loss of CDCP1, which dynamically regulates β1-integrin in non-adherent cells and which may promote vascular dissemination in patients with advanced prostate cancer.
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http://dx.doi.org/10.1038/s41388-018-0142-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824599PMC
May 2018

Clonal diversity revealed by morphoproteomic and copy number profiles of single prostate cancer cells at diagnosis.

Converg Sci Phys Oncol 2018 Mar 16;4(1). Epub 2018 Jan 16.

USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, United States of America.

Tumor heterogeneity is prevalent in both treatment-naïve and end-stage metastatic castration-resistant prostate cancer (PCa), and may contribute to the broad range of clinical presentation, treatment response, and disease progression. To characterize molecular heterogeneity associated with metastatic PCa, multiplatform single cell profiling was performed using high definition single cell analysis (HD-SCA). HD-SCA enabled morphoproteomic and morphogenomic profiling of single cells from touch preparations of tissue cores (prostate and bone marrow biopsies) as well as liquid samples (peripheral blood and bone marrow aspirate). Morphology, nuclear features, copy number alterations, and protein expression were analyzed. Tumor cells isolated from prostate tissue touch preparation (PTTP) and bone marrow touch preparation (BMTP) as well as metastatic tumor cells (MTCs) isolated from bone marrow aspirate were characterized by morphology and cytokeratin expression. Although peripheral blood was examined, circulating tumor cells were not definitively observed. Targeted proteomics of PTTP, BMTP, and MTCs revealed cell lineage and luminal prostate epithelial differentiation associated with PCa, including co-expression of EpCAM, PSA, and PSMA. Androgen receptor expression was highest in MTCs. Hallmark PCa copy number alterations, including PTEN and ETV6 deletions and NCOA2 amplification, were observed in cells within the primary tumor and bone marrow biopsy samples. Genomic landscape of MTCs revealed to be a mix of both primary and bone metastatic tissue. This multiplatform analysis of single cells reveals several clonal origins of metastatic PCa in a newly diagnosed, untreated patient with polymetastatic disease. This case demonstrates that real-time molecular profiling of cells collected through prostate and bone marrow biopsies is feasible and has the potential to elucidate the origin and evolution of metastatic tumor cells. Altogether, biological and genomic data obtained through longitudinal biopsies can be used to reveal the properties of PCa and can impact clinical management.
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http://dx.doi.org/10.1088/2057-1739/aaa00bDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7363158PMC
March 2018