Hematopoietic stem/progenitor cells (HSPCs) are capable of supporting the lifelong production of blood cells exerting a wide spectrum of functions. Lentiviral vector HSPC gene therapy generates a ...human hematopoietic system stably marked at the clonal level by vector integration sites (ISs). Using IS analysis, we longitudinally tracked >89,000 clones from 15 distinct bone marrow and peripheral blood lineages purified up to 4 years after transplant in four Wiskott-Aldrich syndrome patients treated with HSPC gene therapy. We measured at the clonal level repopulating waves, populations' sizes and dynamics, activity of distinct HSPC subtypes, contribution of various progenitor classes during the early and late post-transplant phases, and hierarchical relationships among lineages. We discovered that in-vitro-manipulated HSPCs retain the ability to return to latency after transplant and can be physiologically reactivated, sustaining a stable hematopoietic output. This study constitutes in vivo comprehensive tracking in humans of hematopoietic clonal dynamics during the early and late post-transplant phases.
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•Hematopoietic reconstitution occurs in two distinct clonal waves•A few thousand HSPC clones stably sustain multilineage blood cell production•Steady-state hematopoiesis after transplant is maintained by both HSCs and MPPs•Natural killer clones have closer relationships to myeloid cells than to lymphoid cells
Biasco et al. report a clonal tracking study on the dynamics and nature of hematopoietic reconstitution in humans after transplant. Using integration sites as molecular tags, they measured, in gene therapy patients, repopulating waves, population size and dynamics, activity of progenitor subtypes during the early and late post-transplant phases, and hierarchical relationships among lineages.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Human colon cancer harbors a small subfraction of tumor-initiating cells (TICs) that is assumed to be a functionally homogeneous stem-cell-like population driving tumor maintenance and metastasis ...formation. We found unexpected cellular heterogeneity within the TIC compartment, which contains three types of TICs. Extensively self-renewing long-term TICs (LT-TICs) maintained tumor formation in serial xenotransplants. Tumor transient amplifying cells (T-TACs) with limited or no self-renewal capacity contributed to tumor formation only in primary mice. Rare delayed contributing TICs (DC-TICs) were exclusively active in secondary or tertiary mice. Bone marrow was identified as an important reservoir of LT-TICs. Metastasis formation was almost exclusively driven by self-renewing LT-TICs. Our results demonstrate that tumor initiation, self-renewal, and metastasis formation are limited to particular subpopulations of TICs in primary human colon cancer. We identify LT-TICs as a quantifiable target for therapies aimed toward eradication of self-renewing tumorigenic and metastatic colon cancer cells.
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► Human colon cancer contains distinct classes of tumor-initiating cells ► Self-renewal is limited to long-term TICs but not tumor transient amplifying cells ► Metastasis formation is exclusively driven by long-term TICs ► TICs home to and persist in the bone marrow
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
BRAF Inhibition in Refractory Hairy-Cell Leukemia Dietrich, Sascha; Glimm, Hanno; Andrulis, Mindaugas ...
New England journal of medicine/The New England journal of medicine,
05/2012, Volume:
366, Issue:
21
Journal Article
Peer reviewed
Open access
The authors report a dramatic response to vemurafenib in a patient with hairy-cell leukemia refractory to nucleosides and rituximab.
To the Editor:
Hairy-cell leukemia (HCL) is a mature B-cell ...lymphoid cancer that is commonly treated with purine analogues.
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Virtually all patients with HCL carry the BRAF V600E mutation, which constitutively activates the MEK–ERK pathway and which can be inhibited in vitro by the mutation-specific BRAF inhibitor PLX-4720.
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BRAF
mutations have been identified in melanoma but are found across cancers.
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An inhibitor of mutated BRAF (vemurafenib) has transformed the treatment of melanoma. It is unclear whether this clinical efficacy can be extrapolated to other cancers.
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,
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We used vemurafenib in a patient with refractory HCL and a pressing need for . . .
To investigate the immune response and mechanisms associated with severe coronavirus disease 2019 (COVID-19), we performed single-cell RNA sequencing on nasopharyngeal and bronchial samples from 19 ...clinically well-characterized patients with moderate or critical disease and from five healthy controls. We identified airway epithelial cell types and states vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In patients with COVID-19, epithelial cells showed an average three-fold increase in expression of the SARS-CoV-2 entry receptor ACE2, which correlated with interferon signals by immune cells. Compared to moderate cases, critical cases exhibited stronger interactions between epithelial and immune cells, as indicated by ligand-receptor expression profiles, and activated immune cells, including inflammatory macrophages expressing CCL2, CCL3, CCL20, CXCL1, CXCL3, CXCL10, IL8, IL1B and TNF. The transcriptional differences in critical cases compared to moderate cases likely contribute to clinical observations of heightened inflammatory tissue damage, lung injury and respiratory failure. Our data suggest that pharmacologic inhibition of the CCR1 and/or CCR5 pathways might suppress immune hyperactivation in critical COVID-19.
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Objective
To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for ...already established risk factors.
Patients and Methods
Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status.
Results
With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval CI 0.678–0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05–62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77–56.41%) and 69.65% (95% CI 68.21–71.1%), respectively. These results were confirmed via cross‐validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean SD N+ probability score 0.58 0.17 vs 0.47 0.15 N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio OR 1.04 per percentage probability, 95% CI 1.02–1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96–35.7; P < 0.001) proved to be independent predictors for LNM.
Conclusion
In our present study, CNN‐based image analyses showed promising results as a potential novel low‐cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even ...outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area.
This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future.
We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review.
We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets.
CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Integrating proteomics into precision oncology Wahjudi, Leonie W.; Bernhardt, Stephan; Abnaof, Khalid ...
International journal of cancer,
15 March 2021, Volume:
148, Issue:
6
Journal Article
Peer reviewed
Open access
DNA sequencing and RNA sequencing are increasingly applied in precision oncology, where molecular tumor boards evaluate the actionability of genetic events in individual tumors to guide targeted ...treatment. To work toward an additional level of patient characterization, we assessed the abundance and activity of 27 proteins in 134 patients whose tumors had previously undergone whole‐exome and RNA sequencing within the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) program of National Center for Tumor Diseases, Heidelberg. Proteomic and phosphoproteomic targets were selected to reflect the most relevant therapeutic baskets in MASTER. Among six different therapeutic baskets, the proteomic data supported treatment recommendations that were based on DNA and RNA analyses in 10% to 57% and frequently suggested alternative treatment options. In several cases, protein activities explained the patients' clinical course and provided potential explanations for treatment failure. Our study indicates that the integrative analysis of DNA, RNA and protein data may refine therapeutic stratification of individual patients and, thus, holds potential to increase the success rate of precision cancer therapy. Prospective validation studies are needed to advance the integration of proteomic analysis into precision oncology.
What's new?
Molecular tumor boards increasingly use DNA and RNA sequence information to identify genetic events in tumors to guide treatment for individual patients. Here, tumor samples from patients with advanced or rare cancers enrolled in the NCT MASTER precision oncology program in Heidelberg, Germany, were retrospectively assessed for expression and phosphorylation of proteins relevant to signaling pathways in six interventional categories applied in the program. Depending on interventional category, proteomic data supported prior therapeutic proposals based on DNA/RNA analysis in 10 to 57 percent and was suggestive of alternative treatment options in most other cases. The findings suggest that proteomic information could help refine therapeutic decisions for advanced‐stage cancer patients.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Immunotherapy with bispecific T-cell engagers has achieved striking success against hematologic malignancies, but efficacy against solid tumors has been limited. We hypothesized that oncolytic ...measles viruses encoding bispecific T-cell engagers (MV-BiTEs) represent a safe and effective treatment against solid tumors through local BiTE expression, direct tumor cell lysis and
tumor vaccination.
To test this hypothesis, we generated MV-BiTEs from the Edmonston B vaccine strain to target two model antigens. Replicative and oncolytic potential were assessed by infection and cell viability assays, respectively. Functionality of virus-derived BiTEs was tested
by complementary binding and cytotoxicity assays.
efficacy of MV-BiTE was investigated using both syngeneic and xenograft mouse models of solid cancers.
We verified secretion of functional BiTE antibodies by MV-BiTE-infected cells. Further, we demonstrated therapeutic efficacy of MV-BiTE against established tumors in fully immunocompetent mice. MV-BiTE efficacy was associated with increased intratumoral T-cell infiltration and induction of protective antitumor immunity. In addition, we showed therapeutic efficacy of MV-BiTE in xenograft models of patient-derived primary colorectal carcinoma spheroids with transfer of peripheral blood mononuclear cells.
MV-BiTE treatment was effective in two distinct models of solid tumors without signs of toxicity. This provides strong evidence for therapeutic benefits of tumor-targeted BiTE expression by oncolytic MV. Thus, this study represents proof of concept for an effective strategy to treat solid tumors with BiTEs.
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Wiskott-Aldrich syndrome (WAS) is characterized by microthrombocytopenia, immunodeficiency, autoimmunity, and susceptibility to malignancies. In our hematopoietic stem cell gene therapy (GT) trial ...using a γ-retroviral vector, 9 of 10 patients showed sustained engraftment and correction of WAS protein (WASP) expression in lymphoid and myeloid cells and platelets. GT resulted in partial or complete resolution of immunodeficiency, autoimmunity, and bleeding diathesis. Analysis of retroviral insertion sites revealed >140,000 unambiguous integration sites and a polyclonal pattern of hematopoiesis in all patients early after GT. Seven patients developed acute leukemia one acute myeloid leukemia (AML), four T cell acute lymphoblastic leukemia (T-ALL), and two primary T-ALL with secondary AML associated with a dominant clone with vector integration at the LMO2 (six T-ALL), MDS1 (two AML), or MN1 (one AML) locus. Cytogenetic analysis revealed additional genetic alterations such as chromosomal translocations. This study shows that hematopoietic stem cell GT for WAS is feasible and effective, but the use of γ-retroviral vectors is associated with a substantial risk of leukemogenesis.
One prominent application for deep learning–based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask ...common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data.
The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured.
Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it.
The benchmark contains three data sets—Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)—and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations.
This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.
•AI-based skin cancer classifier testing is often limited to holdout data.•However, holdout testing can mask severe susceptibilities and shortcomings.•Therefore, testing on out-of-distribution (OOD) data should become standard practice.•We release a dermoscopic skin cancer image benchmark designed for OOD testing.•This benchmark incorporates various distribution shifts such as corrupted images.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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