We hypothesized that spatial distribution rather than density of B cells within tumors may provide prognostic significance. We developed statistical techniques  to analyze the spatial distribution of tumor-infiltrating lymphocytes (TILs) in human triple-negative breast cancer (TNBC). Our results indicate that B cells in good outcome tumors (no recurrence within 5 years) are spatially dispersed, while B cells in poor outcome tumors (recurrence within 3 years) are more confined. While most TILs are located within the stroma, increased numbers of spatially dispersed lymphocytes within cancer cell islands are associated with a good prognosis. B cells and T cells often form lymphocyte clusters (LCs) identified via density-based clustering. LCs consist either of T cells only or heterotypic mixtures of B and T cells. Pure B cell LCs were negligible in number. Compared to tertiary lymphoid structures (TLS), LCs have fewer lymphocytes at lower densities. Both types of LCs are more abundant and more spatially dispersed in good outcomes compared to poor outcome tumors. Heterotypic LCs in good outcome tumors are smaller and more numerous compared to poor outcome. Heterotypic LCs are also closer to cancer islands in a good outcome, with LC size decreasing as they get closer to cancer cell islands. These results illuminate the significance of the spatial distribution of B cells and LCs within tumors.While tumor infiltration by CD8 While the prognostic value of the density of CD3+ and CD8+ T cells infiltrating tumors is well known, e.g., recurrence in colorectal carcinoma5, the clinical significance of B cell density in tumors is less clear7. Recent studies indicate that B cells in tumors, especially in conjunction with organized lymphoid aggregates known as tertiary lymphoid structures (TLS), are significant predictors of response to immunotherapy, i.e., immune checkpoint blockade, in melanoma9, soft tissue sarcomas10, and renal cell carcinoma9. However, the possible clinical relevance of non-TLS B cells in tumors should not be overlooked as B cells can perform a variety of functions, including antibody production, cytokine secretion, and antigen presentation to helper T cells.The evolution and progression of cancer is dependent on the tumor microenvironment (TME), which consists of cancer cells, collagen fibers, blood vessels, lymph vessels, fibroblasts, and various types of immune cells. High densities of tumor-infiltrating lymphocytes (TILs) correlate with favorable clinical outcomes in different types of cancer13. Furthermore, spatial heterogeneity could skew the density measured in tissue microarrays (TMA), which only account for a small region within each tumor—such samples may give a poor estimate of the overall cell density, depending on the location of each TMA sample within the tissue. In addition, the TME is spatially heterogeneous with aggregates of cancer cells (cancer cell islands) interspersed with stroma  and patients who had recurrence within 3 years after surgery as poor clinical outcome (n = 12). All patients received standard of care treatments, which included chemotherapy and also radiotherapy in some. None received treatment prior to tumor resection. (See Table We examined immunohistochemistry (IHC)-based images of primary TNBC tumors Fig.  from 36 come n = 2. All pa+), B cells (CD20+), epithelial/cancer cells (PanCK), or “other” (n = 36). Cells were counterstained with DAPI to show the locations of nuclei. In an adjacent slide, a subset of this cohort  was co-stained for CD20+ B cells, CD8+ cytotoxic T cells, CD4+Foxp3+ regulatory T cells (Tregs), and CD4+Foxp3− helper T cells (Th). In this cohort, we will regard CD8+ cytotoxic T cells, CD4+Foxp3+ regulatory T cells, and CD4+Foxp3− helper T cells collectively as CD3+ T cells. A pathologist delineated tumor regions that were deemed representative of the entire tumor in terms of cellularity and TIL distribution, and that were free of artifacts such as tissue folding. Areas of necrotic tumor were avoided, as were peri-tumoral lymphoid aggregates not in close proximity to tumor cells. Given these constraints, regions were chosen to maximize tumor area.Formalin-fixed paraffin-embedded (FFPE) tumor tissues were sectioned  and baked onto glass microscope slides. The following cell phenotypes were identified on the same slide: T cells  to reject the null hypothesis, and receiver operating characteristic (ROC) area under the curve (AUC), both standard statistical measures of a binary classifier14.The mean and median values for various quantities were calculated for each patient and then the average of these values for a given cohort, e.g., good outcome patients, was reported. Statistical significance was ascertained using the two-sided Student’s 7, mean CD20+ B cell density in tumors was not significantly different between good and poor outcome (good (n = 24): 2.4 × 102/mm2; poor (n = 12): 2.2 × 102/mm2; p = 0.8, ROC AUC = 0.6)  were non-significant for clinical outcome patients  Fig. . Relapsents Fig. .+ B cells in cancer cell islands ) was significantly higher in good compared to poor outcome (good (n = 24): 23 B cells/mm2; poor (n = 12): 1.9 B cells/mm2; p = 0.002) : 29 T cells/mm2; poor (n = 12): 56 T cells/mm2; p = 0.007) , was not clinically significant (good (n = 24): 33 stromal B cells/mm2; poor (n = 12): 27 stromal B cells/mm2; p = 0.6).While the clinical significance of B cell density within the entire tumor tissue was not significant, this does not preclude the possible clinical significance of local density of B cells within distinct locations, i.e., in cancer islands (CI) or stroma. Indeed, the density of CD2002) Fig. . Clinica07) Fig. . In cont+ B cells in stroma is not clinically significant, we hypothesized that their spatial patterns may be informative. We investigated the spatial distribution of CD20+ B cells by developing two statistical techniques: occupancy and FD difference16. Both techniques are quantitative measures of the spatial dispersion of cells. We use the terms “spread out”, “spread apart”, and “spatial dispersion” synonymously to connote both separations between cells as well as coverage of the tissue or tissue region in question since lymphocytes are not contiguous throughout the entire tissue. Although the density of CD20+ B cells in stroma versus square size L for 36 patients. Of interest, B cells are not randomly Poisson distributed within tumors (black line). Occupancy of CD20+ B cells in normal breast tissue (gray) is compared to that of stromal CD20+ B cells within tumors from good outcome patients (blue) and poor outcome patients (red). The difference in occupancy between patients with good and poor clinical outcome reflects, to some extent, the minor difference in B cell density since a higher density corresponds to a higher probability that a square will be assigned a ‘1’ in computing occupancy. The area under the occupancy curve (occupancy AUC) is correlated with outcome  as shown in Fig. + B cells are more spatially dispersed for good outcome and, in contrast, more spatially confined for poor outcome.Using our occupancy analysis  of squares with 1’s is proportional to 1/Ld, where d is one way to define the FD17. Our definition of FD is slightly different  and large (200–600 μm) length scales, the slope of straight lines fitting the data on this log-log plot corresponds to the FD. Large FD, i.e., close to two, corresponds to a more spatially uniform and area-filling spatial distribution of cells. FD is larger for the good clinical outcome than for the poor outcome at large length scales, but this trend is reversed at small length scales. This is key to the FD difference described below which demonstrates that CD20+ B cells are more spatially dispersed. As expected, randomly placed points (uniform Poisson process) have a FD of two at length scales large compared to the average separation of points. Further details are given in the “Methods” section.To introduce FD, the number of tiles, each with area Ls in FD between large and small length scales: Δs = sLarge − ssmall, where sLarge is the FD at large length scales and ssmall is the FD at small length scales. The small and large length scales should roughly bracket the typical, or median, nearest neighbor distance between cells of the same type, e.g., CD20+ B cells. Since the mean nearest neighbor distance between B cells is about 60 μm, we determine ssmall (sLarge) over the range 10–40 (200–600)μm. In all cases examined, Δs > 0. Large Δs indicates that cells are more dispersed, i.e., more spatially spread out and separated because they appear more two-dimensional at large length scales and more zero-dimensional (point-like) at small length scales.  for good (n = 24) outcome, indicating that stromal B cells are spatially spread out, but small (Δs = 0.46) for poor (n = 12) outcome, indicating that stromal B cells are spatially confined. Figure p = 3 × 10−3, ROC AUC = 0.77) , indicating that spatial distribution of B cells is an independent predictor of clinical outcome.FD difference Δ25. LCs are identified as containing at least five lymphocytes (T and/or B cells) within a circle with a diameter of 40 μm, corresponding to the minimum size containing the centers of five contiguous cells along a line assuming a cell diameter of 10 μm  μm Fig. . An exam μm Fig.  or spati μm Fig. . This isn = 24): 0.64; poor (n = 12) 0.76; p = 0.04). The density of isolated lymphocytes (p = 0.006), as well as isolated CD20+ B (p = 0.02) and CD3+ T cells (p = 0.007), infiltrating cancer cell islands is significantly higher in good outcome (n = 24) vs. poor outcome (n = 12)  and FD difference , is significantly larger in good outcome  Fig. .  vs. poor (n = 12) outcome as indicated by occupancy AUC as well as FD difference : 0.27; poor (n = 12) 0.33; p = 0.4). While the density of isolated lymphocytes (p = 0.004), as well as isolated CD3+ T cells (p = 0.002), within stroma is significantly higher in good outcome (n = 24) vs. poor outcome (n = 12) tumors, the density of isolated stromal CD20+ B cells is not significant (p = 0.5) for outcome  and FD difference (p = 0.006), is significantly larger in good vs. poor outcome .Only a small fraction of lymphocytes in the stroma are isolated . Less than 1% of LCs are purely B cells and these are very small clusters. Fifty-seven percent of LCs are purely CD3+ T cells while 42% are a mixture of T and B cells with no significant difference in these percentages between good and poor outcomes.LCs, which are almost entirely in the stroma, either consist purely of CD326. While TLS are a subset of LCs, most of the LCs identified using our clustering algorithm are morphologically distinct from TLS and not as densely packed. Most LCs have much fewer lymphocytes at a lower density than TLS. The neighbors of lymphocytes in TLS are other lymphocytes while the neighbors of lymphocytes in non-TLS LCs can be a variety of cell types including fibroblasts, cancer cells, and other lymphocytes. Examination of tissue samples immunostained for high endothelial venules (PNAd) and for dendritic cells (DC-LAMP)  also supports that most LCs are not TLS (data not shown). We defined TLS as containing at least 200 lymphocytes in a circle of radius 70 μm because TLS identified in this way agree with those we can identify visually by immunostaining and with the concept that TLS consist of large groups of densely packed lymphocytes : 0.24; poor (n = 8): 0.27; p = 0.7), the fraction of tissue area occupied by TLS (good (n = 18): 0.01; poor (n = 8): 0.01; p = 0.8), the fraction of all lymphocytes belonging to TLS (good (n = 18): 0.11; poor (n = 8): 0.14; p = 0.5), nor in the fraction of TLS lymphocytes that are B cells (good (n = 18): 0.50; poor (n = 8): 0.60; p = 0.2). Furthermore, most B cells are not in TLS; the majority (about 85%) of B cells are in (heterotypic) LCs. We investigated LC properties, e.g., composition, size, spatial distribution, and location relative to cancer islands, to determine their clinical relevance.Recently, TLS together with B cells are predictive of response to immune checkpoint blockade (ICB) in metastatic melanoma tumors+ B cells (good (n = 24): 0.85; poor (n = 12): 0.84; p = 0.7) and CD3+ T cells (good (n = 24): 0.69; poor (n = 12): 0.78; p = 0.5) within tumors are found within heterotypic LCs. The fraction of heterotypic LCs with more than 30% B cells is significantly larger in poor outcome (good (n = 24): 0.25; poor (n = 12): 0.37; p = 0.02)  Fig. . This isn = 24): 960/cm2; poor (n = 12): 520/cm2; p = 0.04) : 1420/cm2; poor (n = 12): 517/cm2; p = 0.005)  Fig. . Similar05) Fig. .p = 0.8, median: p = 0.8), though for pure T cell LCs, median size was significant . Mann–Whitney test shows that distributions of heterotypic and pure T cell LCs are significantly different (p < 2 × 10−16). However, pure T cell LCs are typically much smaller than heterotypic LCs.Distribution of cluster sizes, i.e., number of lymphocytes per cluster, of heterotypic LCs indicates that good outcome tumors have a greater number of smaller clusters than poor outcome tumors Fig. . Cumulatp = 0.02; FD difference: p = 0.008), indicating that B cells in LCs are more spatially dispersed in good outcome and more spatially confined in poor outcome, similar to our results above for B cells  cells in LCs was ascertained using occupancy AUC and FD difference, which were calculated by assigning a ‘1’ to squares that had at least one B (or T) cell that was in a heterotypic LC. Both quantities are significant for B cells  but FD difference is not significant (p = 0.3)  FD = 1.3; poor (n = 12) FD = 1.1; p = 0.04) and small length scales will be less for T cells than for B cells in heterotypic LCs. Lower B cell density produces a lower FD for small square sizes in the range of 10–40 μm (good (n = 24) FD = 0.67; poor (n = 12) FD = 0.78; p = 0.2), resulting in a larger difference in FD between large length scales (good (n = 24) FD = 1.1; poor (n = 12) FD = 0.90; p = 0.1) and small length scales.The clinical significance of FD difference of B cells in heterotypic LCs is illustrated by the RFS plot in Fig. .3) Fig. . FD diff+ T cells are in pure T cell LCs. There is no significant difference in the fraction of T cells in pure T cell LCs between good vs. poor outcome tumors (p = 0.6). However, occupancy AUC and FD differences of pure T cell LCs was significantly higher in good outcome , indicating that these T cells are more spatially dispersed in good outcome  of CD3ome Fig. . Of noten = 24) 0.80; poor (n = 12): 0.91; p = 0.05), we investigated whether the spatial distribution of LCs was dictated by exclusion from cancer cell islands. So we computed the occupancy and FD difference of stromal cells which, by definition, are not within cancer cell islands. Occupancy AUC of stromal cells is not significantly different between good vs. poor outcome tumors (p = 0.9)   Fig. . Similar.3) Fig. , indicatn = 24): 91 μm; poor (n = 12): 200 μm; p = 0.002; pure T cell LCs: good (n = 24): 73 μm; poor (n = 12): 180 μm; p = 8 × 10−5)   Fig. . The cli04) Fig. . This co10. However, their role in breast tumors remained unclear. Our results indicate that while the overall density of B cells in TNBC tumors is not clinically significant, B and T cell density within cancer cell islands is significantly higher in good outcome tumors. One speculative reason for TIL infiltration into cancer cell islands is that the cancer islands arise from tumor self-seeding by circulating cancer cells that bring associated lymphocytes with them27, . Another possibility is that cytokines and chemokines specifically recruit lymphocytes to cancer islands. In addition, the higher density of B cells infiltrating cancer cell islands in good outcome may be due to a greater diversity of B cell receptors and recognition of cognate tumor antigens. Helmink et al.9 found an increased diversity of B cell receptors in responders to immunotherapy compared to non-responders.Recent studies highlight the clinical significance of B cells in certain human tumors, specifically melanoma and sarcoma29. Interestingly, for the luminal subtype of breast cancer, only intraepithelial TILs, not stromal TILs, were indicative of pathological response29. However, the established guidelines for evaluating TILs in breast cancer discourage using intraepithelial TILs for prognostication because, compared to stromal TILs, their paucity makes them rarer and harder to detect30.Our observation that the density of B and T cells within cancer cell islands is associated with clinical outcome is consistent with previous TNBC studies of neoadjuvant therapy that found densities of both stromal and intraepithelial TILs predictive of pathological responseBeyond cell density, B and T cells are more spatially dispersed, within both cancer cell islands and stroma, in good outcome. Consistent with other studies, the majority of TILs are found in stroma. Importantly, most TILs form LCs that are either heterotypic (mixed T and B cells) or pure T cells. LCs comprised of only CD20 + B cells were rare. Both types of LCs are more abundant and more spatially dispersed in good outcome vs. poor outcome. In good outcome tumors, heterotypic LCs are smaller and more numerous compared to poor outcome. These attributes reflect the spatial distribution of LC constituent B and T cells and cannot be attributed to differences in the spatial distribution of cells constituting the stroma where most lymphocytes are found. Heterotypic LCs are closer to cancer islands in good outcome, with LC size (population) decreasing as they get closer to cancer islands. Although segregation of B and T cells is similar in heterotypic LCs and TLS, the number and density of lymphocytes in the majority of heterotypic LCs are too small for these heterotypic LCs to qualify as TLS. Given that TLS with high lymphocyte density are linked to good prognosis, it is somewhat ironic that good outcome is associated with isolated B cells within cancer cell islands and with spatially dispersed B cells, both of which connote low local concentrations of B cells and are the antithesis of high B cell density germinal centers found in mature TLS.+ B cells are not antibody-producing plasma cells since plasma cells do not express CD20. It is possible that CD20+ B cells act as antigen-presenting cells (APCs) within tumors. Spatial dispersion of CD20+ B cells in good outcome is consistent with their ability to forage over a large region, including in cancer islands, to gather antigens that can be presented to Th cells in heterotypic LCs and TLS. Th cells comprise a substantial fraction of the lymphocytes in TLS and LCs. If CD20+ B cells function as APCs, this would be consistent with the paucity of LCs consisting purely of B cells. APC function may also explain why the density of B cells is not correlated with clinical outcome: a single B cell can activate many Th cells if it can spatially disperse.The biological role of B cells within tumors remains unclear. CD20+ B and CD3+ T cells is not clear. This does not appear to be due to differences in the physical topography of the stroma as we did not find clinically significant differences in the spatial distribution of stroma. Furthermore, immunohistochemical staining of collagen I did not show differences in the spatial distribution of collagen I between good and poor outcomes (data not shown). Spatially aggregated lymphocytes in poor outcome may be associated with immunoediting, resulting in immune-evasive cancer cell clones31. Less spatial dispersion and larger LCs suggest a lack of motility, e.g., in overcoming physical barriers in the stroma such as collagen fibers, due to a lack of chemokine signaling and cognate antigens, or a lack of metabolites due to poorly organized leaky vasculature that produces hypoxic regions, or metabolic insufficiency from dysfunctional depolarized mitochondria that have been associated with exhausted CD8+ TILs32. It has been suggested that cells are more motile if they are elongated rather than compact and round35. Future studies should look for correlations between lymphocyte clustering and cell shape, e.g., the aspect ratio or the ratio of the cell perimeter to the square root of the area of a cell35. Another finding from this study is that the spatial distribution of TILs is fractal. We speculate that this fractal pattern may arise from branching trajectories that B and T cells follow as they avoid obstacles and follow along blood vessels and collagen fibers37.The source of clinically significant differences between good and poor outcome tumors in the spatial distribution of CD20Our techniques minimize the noise arising from sparseness of discrete points that correspond to cell locations because they divide the tissue area into squares and ask binary questions that do not depend on the exact location and number of cells in a square. Thus, they effectively average over the larger region of a square. Similarly, density-based clustering, used to identify LCs, depends on the local density of several cells and is basically a form of averaging.39, locating immune cell clusters or “hotspots”41, determining the amount of infiltration of lymphocytes into a tumor42, and using Shannon entropy to quantify the cellular diversity43. In addition, FDs44 have been used to characterize the irregular morphology of tumors20 and tumor-related structures24, but not the spatial distribution of immune cells. Spatial heterogeneity connotes local variations in density; this implies a certain size  of a region in which density is calculated. All these approaches produce measures of the spatial distribution at a single length scale. The statistical techniques that we have developed move beyond this limitation to quantify how the spatial distribution varies with length scale and then use this to determine whether the cells are clustered or spatially dispersed. On a more general level, these approaches are flexible and applicable to a broad spectrum of problems that go beyond the spatial distribution of cells or discrete entities. More complex binary questions can be employed to explore the proximity of cells and structures indicative of possible function, e.g., regarding co-localization of at least one T cell and a dendritic cell (suggesting antigen presentation by the dendritic cell) or proximity of at least one B cell and one T cell near a blood vessel (suggesting lymphocyte extravasation).Previous efforts to quantify the spatial heterogeneity of the TME include measuring the spatial co-localization of tumor and immune cellsWhile our results are suggestive, they should be interpreted with caution due to some limitations. The first is the small patient sample size; follow-up studies should involve a much larger cohort of patients. Second, the TNBC patients were somewhat diverse in their disease characteristics and were not uniformly treated after tumor resection, though none had treatment before surgery.Nevertheless, our results indicate the importance of the spatial distribution of B cells within human TNBC tumors with regard to clinical outcomes. These findings raise new questions about the biology and clinical impact of TILs. Of particular interest is the discordant significance of B cell density vs. spatial distribution on the outcome of TNBC. The role of B cells within tumors is understudied and remains unclear, partially due to prior reports that their density within tumors is of no significance. Our finding of the strong correlation of B cell spatial distribution with clinical outcome in TNBC revives their significance in cancer, and opens up new questions on their functional role in preventing recurrence.® Gold Antifade mounting media . All the images were acquired using the Vectra 3.0 Automated Quantitative Pathology Imaging System (Akoya Biosciences) and commercially available software packages  were used to identify each cell, define its type (cancer or specific immune), and assign it Cartesian coordinates. Using an automated tissue segmenter algorithm built in inForm®, we further divided the images into areas of cancer islands and stroma based on anti-pan cytokeratin antibody staining.Specimens were identified through an IRB-approved protocol via the City of Hope (COH) Biospecimen Repository which is funded in part by the National Cancer Institute. The IRB granted a waiver of informed consent from all human participants because de-identified tissue samples were obtained from the CoH Biospecimen Repository. Samples from patients diagnosed with TNBC and treated at COH from 1 January 1994 to 4 March 2015 were retrieved. Eligible patients had the following features: stage I-III breast cancer; at least one tumor biospecimen was available from the initial surgical resection or biopsy; clinical outcome data were available for identification of relapse-free survival; no prior treatment at the time of surgical biopsy. Archived FFPE tumor tissues were sectioned  and on the same slide, multi-color immunostaining was performed including anti-pan cytokeratin , anti-CD8 , anti-CD3 , anti-FOXP3  and anti-CD20 . Samples were further counterstained with DAPI to visualize the nuclei of all cells. Prior to imaging, the tissue sections were coverslipped with ProLong46.Using the data spreadsheets with every cell phenotyped and given a set of Cartesian coordinates, we tiled each image with a grid of identical squares. We omitted squares that had no cells, since they lay outside the tissue. However, we included squares that were within the tissue but contained no cells of interest. The length L of one side of a square varies from 10 to 600 μm. A square was assigned 1 or 0 according to the answer to a binary question. For comparison we calculated the occupancy FD of a uniform random (Poisson) distribution of points. We processed the data using in-house custom-developed software (RStudio) and the R packages “spatstat” for point patterns, “dbscan” for cluster analysis, and “EBImage” for raster imagesL2. A ‘0’ or ‘1’ is assigned to each square according to the answer to a binary (yes-no) question, e.g., “Is there at least one B cell in the square?”  as described above, with 1’s and 0’s corresponding to the answers to a binary question. The number n(L) of squares with ‘1’ will be proportional to (1/Lδ) where the exponent δ is one type of FD and is ≤217. If the system is self-similar and fractal over a range of length scales L, n(L) will follow a power law: n(L) = A/(Lδ), and the exponent δ will remain constant as L varies. The constant of proportionality, A, depends on the size of the tissue that dictates the number of boxes covering the tissue. To avoid this, we define the FD as follows. In a plot of log [n(L)] versus log[A/L], where A is a constant, the FD is the slope s of a line fit through the points using linear regression with a least-squares fit, i.e., s(L) = −d[log n(L)]/d[log L].  The FD over a range of values of L is determined for each patient and then averaged over all patients with a given clinical outcome.FDs can be used to characterize the number of grid squares with 1’s and hence, the spatial distribution of cells. While there are a number of different ways to define the FD, we chose a variation of the box-counting method+ B cell (or T cell) in a cancer cell island were assigned a ‘1’, while squares covering stroma had zeros. The fraction of squares with 1’s, i.e., the occupancy, was the number of squares with 1’s divided by the total number of squares covering the image (including the stroma). This can easily be generalized to calculating the occupancy and FD of lymphocytes in the stroma or in the whole tissue.In the calculations of the occupancy and FD with a grid of squares covering the tissue, squares with at least one CD20y-axis is Po,i(s)/No,i where s is the number of lymphocytes in an LC, No,i is the total number of lymphocytes in LCs of type i (i = pure T LCs or heterotypic LCs) and outcome o (o = good or poor), and Po,i(s) is the number of LCs (of type i and outcome o) of size s. The normalization condition iss’ of the fraction of lymphocytes in a cluster of size s’ must equal unity. Blue line is for good outcome and red line is for poor outcome. Insets: Cumulative fraction of lymphocytes that belong to LCs of size s or smaller versus s. The x-axis is on a logarithmic scale. The cumulative fraction is given by the expressionIn Fig. Throughout the paper, the threshold for RFS plots was systematically chosen so that two thirds of the patients are in the upper curve and one third are in the lower curve. These fractions correspond to the proportions of good and poor outcome patients, respectively.Further information on research design is available in the Reporting Summary


Previously published sequencing data from 573 metastasis samples were also analyzed. Through transcriptome profiling and sample classification based on a set of predefined MetA‐C‐differentiating genes, we found that most metastases were heterogeneous for the MetA‐C subtypes. Overall, MetA was the most common subtype, while MetB was significantly enriched in castration‐resistant samples and in liver metastases, and consistently associated with poor prognosis. By gene set enrichment analysis, the phenotype of MetA was described by high androgen response, protein secretion and adipogenesis, MetB by high cell cycle activity and DNA repair, and MetC by epithelial‐to‐mesenchymal transition and inflammation. The MetB subtype demonstrated single nucleotide variants of RB transcriptional corepressor 1 (RB1) and loss of 21 genes at chromosome 13, including RB1, but provided independent prognostic value to those genetic aberrations. In conclusion, a distinct set of gene transcripts can be used to classify prostate cancer metastases into the subtypes MetA‐C. The MetA‐C subtypes show diverse biology, organ tropism, and prognosis. The MetA‐C classification may be used independently, or in combination with genetic markers, primarily to identify MetB patients in need of complementary therapy to conventional androgen receptor‐targeting treatments.To improve treatment of metastatic prostate cancer, the biology of metastases needs to be understood. We recently described three subtypes of prostate cancer bone metastases (MetA‐C), based on differential gene expression. The aim of this study was to verify the clinical relevance of these subtypes and to explore their biology and relations to genetic drivers. Freshly‐frozen metastasis samples were obtained as hormone‐naive , based on their transcriptomic profiles. This study verifies the clinical relevance of the MetA‐C subtype classification and demonstrates its association with patient prognosis and tumor biology in several separate cohorts. The MetA‐C classification may be used to identify MetB patients in need of complementary therapy to conventional androgen receptor‐targeting therapies. ADTandrogen deprivation therapyARandrogen receptorARSIandrogen receptor signaling inhibitorsCNVcopy number variantEMTepithelial‐to‐mesenchymal transitionGSEAgene set enrichment analysismCRPCmetastatic castration‐esistant prostate cancerMetmetastasisNEPCneuroendocrine prostate cancerNESnormalized enrichment scoresOPLS‐DAorthogonal projections to latent structures discriminant analysisPARPpoly‐ADP ribose polymerasePCprostate cancerPCAprincipal components analysisPSAprostate specific antigenRMArobust multi‐array averageSNVsingle nucleotide variant1Prostate cancer (PC) is the most common malignancy in men and a major cause of cancer mortality worldwide. Patients with lethal PC develop bone metastatic disease that is primarily treated with androgen deprivation therapy (ADT). In most cases, ADT reduces metastasis growth, but eventually metastatic castration‐resistant PC (mCRPC) develops. Several treatment strategies for mCRPC exist of which the majority aim at inhibiting androgen receptor (AR) signaling, while others include chemotherapy, immunotherapy, bone‐targeting therapies, and poly‐ADP ribose polymerase (PARP) inhibitors . PatientRB1 alteration as a marker for poor prognosis ,,7], the U tests. Genes showing significant differences (P < 0.05) in both tests were selected for modeling using orthogonal partial least squares discriminant analysis (OPLS‐DA) based on corresponding RNA counts.For analysis of copy number variation (CNV), all coding genes were retained and called as having a gain if the copy number was ≥ 4 and loss if the copy number was ≤ 1. Genes were kept in downstream analyses if they had an RNA count ≠ 0 in > 97% of the samples and had a CNV in ≥ 3% of the samples. Remaining genes were tested for difference in MetA, B, or C fraction levels and RNA levels between samples harboring an CNV and samples with no CNV in the specific gene using Mann–Whitney 33.1Rs = −0.68, P = 1.9E‐11 and Rs = −0.58, P = 1.8E‐8, respectively, n = 103), while the MetB and MetC showed no clear relationship . Replicate samples obtained from the same metastasis showed high concurrence in the estimated fractions of MetA‐C , and that of MetA significantly lower , in CRPC compared with hormone‐naive cases  and at metastasis sampling , while the fraction of MetB showed an inverse relation to serum PSA levels    or chemotherapy Table . However3.3n = 99) and the Abida (n = 332) cohorts and evaluated in relation to available clinical data [n = 266) or capture (n = 208) libraries. All three cohorts gave results that were in good agreement with results from the Clariom D cohort, as specified below. In addition, they provided the possibility of evaluating metastasis content of the MetA‐C subtypes in relation to metastatic site.The content of MetA‐C was estimated also in external metastasis samples profiled by RNA sequencing within the Quigley .3.4To explore biological differences between the MetA‐C subtypes, GSEA was performed, based on the dominating subtype of each sample. Enrichment of hallmark gene sets was analyzed for each subtype in comparison with the other two in all cohorts examined Table , and halRs = 0.76, P = 6.2E‐15), while it was inversely correlated to the proliferation  and NEPC scores . In contrast, the MetB and MetC estimates showed inverse correlations with the metastasis AR activity scores  , while increasing MetC content was associated with decreasing proliferation score  and increasing NEPC score   Table .3.5The estimated MetA‐C content of the metastasis samples of the validation cohorts was evaluated in relation to reported ETS gene fusions , 7, gainRB1  showed significantly higher MetA content than the negative cases, while no clear association between the MetA‐C content and ETS gene fusion status was observed in the Quigley cohort Fig. . The MetRB1 Fig. , while tRB1 Fig. . The MetRB1 Fig. . OverallRB1 Fig. . DiverseRB1 (deleterious SNVs and/or CNV loss) were observed in 63% and 18‐31% of the metastases with a dominant MetB subtype in the Quigley and Abida data, respectively. As previously reported, RB1 aberrations were associated with poor patient prognosis after mCRPC diagnosis  and afty cohort ), but im4By analysis of 676 metastasis tissue samples originating from four patient cohorts, this study verifies the clinical relevance of classifying PC metastases into the transcriptomic subtypes MetA‐C . By estiMost metastases were estimated to be heterogeneous for MetA‐C subtypes, with MetA being most frequently observed. If this heterogeneity represents a mixture of tumor epithelial cell clones with different characteristics, as have been previously demonstrated for primary PC , 16, or The MetA‐C frequencies observed here were comparable to those originally reported . The conRB1 and P53 aberrations [The MetA‐C subtypes were originally identified in bone metastases . Here, trrations , we wereThe GSEA confirmed the biological characteristics of MetA‐C originally described , with MeThe MetA‐C metastasis subtypes show similarities with, but are not identical to , the traRB1 aberrations, would probably benefit from early chemotherapy and treatment with drugs currently being suggested for NEPC, such as cell cycle inhibitors and epigenic modulators [Recently, we identified a DNA methylation signature associated with AR activity in PC bone metastasis samples . High prdulators , 28. Givdulators , and the5PC metastases can be differentiated into clinically relevant subtypes, based on distinct transcriptomic profiles. The MetA‐C subtypes show different biology, organ tropism, and prognosis after AR targeting therapies Fig. . PhenotyET, AB, and PW have a pending patent application . The other authors declare no conflict of interest.ET, LK, JN, and PW conceived and designed the study. LK, JS, HJ, ML, SC, and CTK acquired the data. ET, LK, JS, EF, and PW analyzed and interpreted the data. PW wrote the manuscript draft that all authors critically revised. CTK, JED, AW, SC, AJ, KW, and AB contributed with technical and materiel support. AB and PW supervised the study.https://publons.com/publon/10.1002/1878‐0261.13158.The peer review history for this article is available at Fig S1. Associations between the metastasis subtypes A‐C (MetA‐C) and ETS gene fusion status.Click here for additional data file.Fig S2. Associations between the metastasis subtypes A‐C (MetA‐C) and deleterious single nucleotide variants (SNVs).Click here for additional data file.Table S1. Patient and sample characteristics.Click here for additional data file.Table S2. Gene transcripts used for classification of the metastasis subtypes A‐C (MetA‐C).Click here for additional data file.Table S3. Estimated fractions of the metastasis subtypes A‐C (MetA‐C) in replicate samples collected from the same metastatic site at one time‐point.Click here for additional data file.Table S4. Estimated fractions of the metastasis subtypes A‐C (MetA‐C) in samples collected from the same patient at two different time‐points.Click here for additional data file.Table S5. Estimated fractions of the metastasis subtypes subtypes A‐C (MetA‐C) in relation to tumor and patient characteristics of the Abida polyA, Abida capture, and Quigley metastasis cohorts.Click here for additional data file.Table S6. Hallmarks being consistently enriched in the metastasis subtypes A‐C (MetA‐C), respectively, in the Clariom D, Abida capture, Abida polyA, and Quigley metastasis cohorts.Click here for additional data file.Table S7. Copy number variation (CNV) gains and losses of coding regions in relation to corresponding transcript levels and estimated fractions of metastasis subtypes A‐C (MetA‐C) in samples of the Quigley, Abida polyA and Abida capture cohorts.Click here for additional data file.


Abnormal inflammation coagulation biomarker levels of troponin, C‐reactive protein (CRP), and D‐dimer levels in serum have been demonstrated to be associated and involved in the disease progression of coronavirus disease 2019 (COVID‐19).d‐dimer, white blood cell (WBC) and polymerase chain reaction–cycle threshold (PCR‐Ct) within COVID‐19 survivors  and in deceased  group. Also, assessing any differences between both groups in studied parameters. Second: a correlation study of studied parameters' level has been conducted within families  that lost more than one member due to the severity of the disease. Also, differences between these family and control group  group in studied parameters have been assessed.First: the study aimed to investigate the correlation of troponin, CRP, r = 0.75, p < 0.0001), and to troponin in deceased male patients . The second week of patient admission was critical in the group of families who lost more than one person, when troponin was correlated positively with D‐dimer, CRP, and WBCs.In the first week of hospitalization, there were significant differences in D‐dimer, CRP and troponin level between survived and deceased patient groups. In the second week of the admission, both groups had significant differences in the level of all studied parameters; troponin I, D‐dimer, CRP, and WBCs. WBC levels positively correlated to CRP in male survivors , and white blood cells (WBCs) level were significantly higher in COVID‐19 patients who died than in COVID‐19 survivors. High troponin and WBC levels, were considerably associated with families that lost more than one member, when compared with the unrelated COVID‐19 patient control. We also compared the levels of those parameters between families with higher mortality and other COVID‐19 patients. Additionally, correlations among some parameters, such as age, PCR‐Ct, WBCs, CRP, D‐dimer, and troponin, in COVID‐19 patients in general were also investigated. Furthermore, correlations among the studied parameters were examined for survivors and deceased patients, taking into account the effect of gender in each group. Finally, the differences in those parameters between survivors and non‐survivors were studied and compared between the first and second weeks of admission.22.1The first study included 173 hospitalized (severe) COVID‐19 confirmed patients  who were divided into two categories: 143 survivors  and 30 deceased . The same patients were re‐categorized in the second study into two different categories: seven families who experienced more than one death in hospital (41 patients), and a control group of 132 other hospitalized confirmed cases  of the National Health Commission,According to the 2.2,Real‐time RT‐PCR to detect SARS‐CoV‐2 was performed using a viral RNA template extracted from nasopharyngeal swab samples. After collection, the total RNA was automatically isolated within 45 min using the Qiagen EZ1 Advanced XL system (Qiagen). The presence of SARS‐CoV‐2 was then detected by RT‐PCR amplification of fragments of the SARS‐CoV‐2 open reading frame 1ab (ORF1ab) and envelope (E) genes, using the PowerChek SARS‐CoV‐2 Real‐Time PCR Kit (KogeneBiotech). Thermocycling was performed on a Rotor‐Gene Q thermocycler (Qiagen), with the conditions for amplification being 50°C for 30 min, 95°C for 10 min, 40 cycles of 95°C for 15 s, and then 60°C for 1 min. When the two target genes (ORF1ab and E) tested positive by specific real‐time RT‐PCR, a cycle threshold value  ≤ 36.7 was defined as a positive result and a Ct‐value of greater than 36.7 was defined as a negative result.2.3The biological marker tests conducted in this study included complete blood count (CBC), CRP, D‐dimer, and troponin I. The CBC was primarily performed using a Medonic M‐Series haematology analyzer  and quantitative immunological determination of CRP in serum was performed using the cobas c111 (Roche Diagnostics) system. Measurement of CRP aids in evaluating the amount of inflammation, with normal values for adults being <5.0 mg/L. Fluorescence immunoassay ichroma™ II (Boditech Med Inc.) was used for the D‐dimer Rapid Quantitative Test, with a normal value being <500 ng/ml. Cardiac troponin I tests were analyzed by the cobas e 411 analyzer (Roche Diagnostics), which is a fully automated analyzer that uses patented ElectroChemiLuminescence technology for immunoassay analysis. The normal range for troponin is between 0 and 0.4 ng/ml.2.4d‐dimer, CRP, WBCs, and PCR‐Ct in each studied COVID‐19 group. One‐way ANOVA was used to analyze differences in the investigated parameters between the two groups.Pearson correlation and polynomial regression were employed to analyze the correlations among troponin I, 33.1The first part of the current study was conducted on 173 COVID‐19 hospitalized (severe) patients, of which 91 52.6%) were male and 82 (47.4%) were female. This gender infection rate with SARS‐CoV‐2 is similar to that of other studies..6% were The rate of death among females  in the current study was generally higher than that of males . This result conflicts with data from a study conducted with more than 1500 COVID‐19 patients from different areas of the world, which concluded that male patients have a higher mortality rate than females.In the first week of admission to the hospital, there was no correlation among any studied parameters Figure . This war = 0.73, p < 0.0001) between WBCs and CRP  was observed between WBCs and D‐dimer in female patients  between WBCs and CRP in male survivors , neither survivors nor deceased patients showed any correlation among the studied parameters during their first week of hospital admission Figure . In the s Figure . CRP is r = 0.73, p = 0.007) was observed between WBCs and troponin in deceased male patients  between survivors (57.8 years) and those who died  between both groups in D‐dimer (226.2 ± 41.53 ng/ml) and CRP (5.000 ± 0.9403 mg/L) levels. d‐dimer elevation has been linked to coagulopathy and, with disseminated intravascular coagulation,In the first week of patient admission, there was a highly significant difference , WBCs , and PCR‐Ct  between survivors and deceased COVID‐19 patients, with the exception of PCR‐Ct. Mean differences between survivors and those who died were 0.7782 ± 0.08275 for troponin, 1685 ± 176.4 for d‐dimer, 25.10 ± 2.500 for CRP, and 10.32 ± 0.8450 for WBC count. When the levels of troponin, D‐dimer, CRP, and WBCs had returned to normal in the survivors, all remained elevated in the patients who died. If we take into account that the PCR‐Ct value was negative for both categories, it may be concluded that secondary infectionIn the second week of hospitalization, all studied parameters differed highly significantly  that lost more than one member due to COVID‐19, with a total of 15 deaths recorded . The second group, which was the control , also recorded 15 deaths , WBC count , CRP , and d‐dimer , but there were no significant differences in troponin levels , WBC count , and CRP . Conversely, there were no significant differences between the groups in d‐dimer and PCR‐Ct  during the first week of hospital admission differed between families that lost more than one member and the control group  and followed the Declaration of Helsinki.Ayad M. Ali performed lab work and data collection; Hassan M. Rostam analyzed the data and wrote the results; Kameran M. Ali, Chalak M. Noori, Mohammed H. Fatah, and Hassan M. Tawfeeq contributed in the writing of the manuscript. All authors approved the final version of the manuscript.Supporting information.Click here for additional data file.


Many children living with HIV (CLWH) display impaired cognition. Although early combination antiretroviral therapy (ART) produces improved cognitive outcomes, more long‐term outcome data are needed. After concluding the Children with HIV Early antiRetroviral (CHER) trial in 2011, we investigated cognitive performance, at seven and nine years of age. Participants had been randomized to deferred ART ; immediate time‐limited ART for 40 weeks  and immediate time‐limited ART for 96 weeks . We also recruited HIV‐exposed uninfected  and HIV‐unexposed  children.Data were collected between May 2012 and December 2017. Mixed‐model repeated‐measures ANOVAs assessed differences over time between CLWH  and CHIV‐ CHEU and CHU between ART‐Early (ART‐40W and ART‐96W), ART‐Def, CHEU and CHU; and between ART‐40W, ART‐96W, ART‐Def, CHEU and CHU.ps < 0.05). The first ANOVAs found that for (a) motor dexterity, CLWH performed worse than CHIV‐ at seven years (p < 0.001) but improved to equivalence at nine years, (b) visual‐spatial processing and problem solving, only CLWH (p < 0.04) showed significant performance improvement over time and (c) working memory and executive function, CLWH performed worse than CHIV‐ at both seven and nine years (p = 0.03 and 0.04). The second ANOVAs found that for (a) working memory, CHU performed better than ART‐Early and CHEU (p < 0.01 and <0.04), and (b) motor dexterity, ART‐Def performed worse than ART‐Early, CHEU and CHU at seven years  but improved to equivalence at nine years (ps > 0.17). Similarly, for motor dexterity, ART‐Def performed worse than ART‐96W, CHEU and CHU at seven years  but improved to equivalence at nine years (ps > 0.20).All comparisons found significant effects of Time for most outcome variables , all ART‐treated children, regardless of treatment arm, remain at risk for cognitive deficits over early school ages. Although the nature of these deficits may change as cognitive development proceeds, there are potential negative consequences for these children’s future learning, reasoning and adaptive functioning. E(CHER) trial.Research into the efficacy of early intervention strategies must go beyond cross‐sectional between‐group comparisons and instead investigate long‐term effects of combination antiretroviral therapy (ART) on the developing child’s cognitive abilities . This loThis trial was conducted in South Africa from 2005 to 2011 , 10. AsyThe CHER trial found that early ART initiation significantly reduced mortality and HIV disease progression . HoweverA CHER neurodevelopmental sub‐study investigated participants at a mean age of 11 months , those iIn another randomized study, the Pediatric Randomized Early versus Deferred Initiation in Cambodia and Thailand (PREDICT) trial  299 chil1.1We investigated cognition at seven and nine years of age in children from the CHER neurodevelopmental sub‐study . Our stu22.1We used an observational‐longitudinal design. Data were collected between May 2012 and December 2017. CLWH had regular three‐monthly adherence visits and clinical evaluations. HIV‐uninfected participants had six‐monthly clinical assessments.2.2At the end of the CHER trial in 2011, CLWH from Cape Town were enrolled in a follow‐up longitudinal neuroimaging and cognitive study . HIV‐uniFigure 2.32.3.1Participants were administered a comprehensive test battery at age seven years and nine years by trained research assistants supervised by a licensed psychologist. Test stimuli and instructions were presented in the participant’s preferred language . Rigorous translation and back‐translation (conducted under license from test publishers) ensured that different versions of the instruments were of equivalent complexity. Assessments were video recorded for quality control.The battery consisted of standardized tests, commonly used in paediatric neuropsychological assessment, with strong psychometric properties and proven utility across different cultural and language settings , 20, 21.Regarding the KABC‐II, we administered 13 subtests to derive indices termed Sequential Processing (an assessment of auditory working memory), Simultaneous Processing , Learning (short‐term memory) and Planning (executive reasoning). Aggregating performance on KABC‐II subtests not relying on verbal expression allows derivation of a Nonverbal Index (NVI). Summing across all KABC‐II subtests allows derivation of a Mental Processing Index . Regarding the TOVA, the primary outcome variable used was the ADHD index score. High scores indicate better functioning (i.e. scores closer to zero on the negative end are better).2.3.2To collect biographic, economic and medical information, a study clinician administered study‐specific questionnaire to the parent/guardian. To measure ART adherence, we used pill count data from the clinic visit closest to each of the test sessions.2.4Research ethics committees at the Universities of Cape Town and Stellenbosch approved the study, which adhered to the principles of the Declaration of Helsinki . The con2.5p‐values are two‐tailed, with α set at 0.05 for decisions regarding statistical significance. We examined distributional assumptions underlying parametric statistical tests for all variables of interest and adjusted analytic plans where necessary. For the Beery‐VMI, KABC‐II and TOVA we used age‐adjusted standardized scores based on US norms. For the PPT, category fluency test and PPVT‐IV we used raw scores.Analyses were conducted using Statistica (version 13). All reported 2.5.1First, chi‐squared tests of contingency, Fisher’s exact tests, one‐way ANOVAs or Kruskal–Wallis tests examined between‐group differences for sample sociodemographic and clinical characteristics. Second, a series of two‐way mixed‐model repeated‐measures ANOVAs, with group assignment and age (seven and nine years) included as fixed effects, participants (nested in groups) included as a random effect, and performance on a cognitive variable as the outcome, allowed simultaneous examination of between‐group differences at each measurement point and change over time. Due to the small sample size and to fully understand the impact of HIV on the developing brain, the first set of models compared CLWH  to CHIV‐ (combined CHEU and CHU). The second set compared an ART‐Early group (combined ART‐40W and ART‐96W groups) to ART‐Def, CHEU and CHU groups. The third set compared the five groups  against one another. We used Fisher’s Least Significant Difference tests for pairwise post hoc comparisons. For each ANOVA, we calculated effect size estimates , which capture the magnitude of the difference in the population. We interpreted effect sizes as being small (0.01), medium (0.06) or large (0.14), by convention . Finally33.1Most child participants (at least 75% in each of the five groups) were raised by their biological mothers. Among both children and mothers, there were no significant between‐group differences in education, with 76% of the child participants in age‐appropriate grades. Relatively few mothers were high school graduates, but most had completed ≥10 years of formal education. All participants were from low socioeconomic status (SES) backgrounds, with similar household income across groups. There was a significant between‐group difference for home language distribution: In the CHU group 37% of participants spoke Afrikaans and the rest Xhosa, whereas in the other groups almost all children spoke Xhosa , weight and height  were asymptomatic. At five years of age in the neurodevelopmental sub‐study, 28 (35.9%) of 78 CLWH had either Centers for Disease Control severe stage B or C disease, 21 (30%) are included in this study. The most common diagnoses were HIV encephalopathy (HIVE), failure to thrive, oesophageal candidiasis and HIV wasting syndrome. All CLWH had been initiated on a first‐line ART (zidovudine/lamivudine/lopinavir‐ritonavir). Sixty‐two of the 69 CLWH (89.85%) were still on the original first line ART regimen at the nine‐year assessment. Overall, 13 children had HIVE ; 11 have since recovered .3.33.3.1Analyses detected a significant main effect of Time, with higher scores at nine years than at seven years, for most outcome variables (see Tables p = 0.03 and 0.04 respectively). Finally, the analyses detected two significant Time by Group interaction effects: for fine motor dexterity (PPT raw score), CLWH performed worse than CHIV‐ at seven years (p < 0.001) but improved to equivalence at nine years. For visual‐spatial processing and problem solving (KABC‐II Simultaneous Processing Index score), CLWH (p = 0.03) but not CHIV‐ showed significant performance improvement over time.There were also two significant main effects of Group: for executive function (semantic fluency raw score) and auditory working memory , CLWH performed worse than CHIV‐  and better than ART‐Def (p = 0.05). Finally, there was one significant Time by Group interaction effect: for fine motor dexterity (PPT raw score), ART‐Def performed more poorly than ART‐Early, CHEU and CHU at seven years , but improved to equivalence by nine years (ps ≥ 0.05).There was one significant main effect of Group: for auditory working memory , CHU performed significantly better than ART‐Early and CHEU , but had the largest improvement over time with no significant pairwise differences at the nine‐year assessment .These analyses detected no significant main effects of Group, and only one significant Time by Group interaction effect: for fine motor dexterity (PPT raw score), ART‐Def performed more poorly than ART‐40W, CHEU and CHU at the seven‐year assessment (ps > 0.08).Finally, within‐group analyses detected no statistically significant effect of home language on any cognitive outcome variable at either seven or nine years of age  with planned interruption, or deferred ART initiation until clinical or immunological disease progression (ART‐Def), according to WHO 2006 ART treatment criteria.The primary value of this study is its longitudinal design, which allowed evaluation of the effects of three ART strategies on cognitive outcomes at two time points and on the trajectory of change between measurements. Moreover, the tests were conducted across a developmentally critical period when the foundations of adult cognitive architecture are being established . Other sIn analyses comparing the cognitive performance of CLWH and CHIV‐ at both seven and nine years, CLWH performed significantly worse on measures of executive functioning  and auditory working memory. Analyses comparing ART‐Early, ART‐Def, CHEU and CHU demonstrated that better auditory working memory performance among uninfected participants was largely attributable to the CHU, who performed significantly better than all CLWH and CHEU.These findings partially confirmed our hypothesis related to the impairing effects of HIV on cognition, and are consistent with a recent report that school‐aged CLWH receiving ART before age three years performed worse on an auditory working memory task than CHIV‐ controls . Of noteet al. from the same cohort [Lastly, cognitive performance of ART‐40W, ART‐96W, ART‐Def, CHEU and CHU groups did not confirm hypotheses related to effects of early intervention. ART‐40W and ART‐96W participants had similar scores to ART‐Def participants at both seven and nine years on almost all outcome measures. These findings support those of Laughton e cohort  in demone cohort .All three series of two‐way mixed‐model repeated‐measures ANOVAs confirmed our hypothesis related to cognitive developmental trajectory most outcomes, participants in all groups performed better at nine years than at seven years. This cohort was assessed at a critical phase of brain growth when details of neuronal circuits are being calibrated for functions required of the adult brain . TherefoUnexpectedly, however, scores for visual‐motor integration, attention and auditory working memory across all five groups were lower at nine years than at seven years. This apparent anomaly is probably explained by using normative tables based on North American standardization samples. Hence, at seven years the current sample of South African children was performing at a level close to that of the seven‐year‐old reference group, whereas at nine years their performance was farther away from the norm . This result points to one of many potential hazards of using foreign normative data to judge the performance of local patients , 36.et al. [et al. [An interesting finding was that, across all between‐group comparisons, there was a significant Time by Group interaction for PPT, which assesses fine motor dexterity. The essence of this result is that at seven years, ART‐Def participants performed significantly worse than the other four groups, whereas at nine years there were no significant between‐group differences (due to a rapid trajectory of improvement in the ART‐Def group). The first part of our result is consistent with Puthanakit et al. , who fou [et al.  in suggeOverall, our results suggest that CLWH, regardless of treatment strategy or degree of viral suppression, require monitoring throughout childhood as they remain at risk for cognitive deficits. Although our results differ from previous studies suggesting that deficits in visual‐motor integration, attention, visuospatial abilities, receptive language and learning abilities are core components of the cognitive profile in CLWH , 38. OurOur study had the following limitations: first, although CLWH participants entered the CHER trial before three months of age, no data are available on whether the transmission was in utero or intra‐partum. Second, all CLWH were ART adherent at testing, with over 80% being virally suppressed. Such levels of adherence are not typical , and so it remains unknown whether more cognitive deficits might be observed in less‐adherent individuals. Third, our test battery did not include direct measures of expressive vocabulary, a cognitive construct that previous studies suggest is an important component of HIV‐associated cognitive impairment in children and adolescents . Fourth,5In summary, our data suggest that neurocognitive performance for treatment groups and controls was similar for most domains by nine years of age, with improvements from seven years in all groups. Nevertheless, all ART‐treated CLWH, regardless of their treatment arm, remain at risk for cognitive deficits over early school ages. This risk is present even though these children experienced active and intense guidance, monitoring, clinical management and sustained viral suppression. Our data also suggest, however, that the nature of these cognitive deficits may change over time as neuropsychological development proceeds (e.g. we observed deficits in fine motor dexterity at seven years but not nine years).et al. [Overall, our findings support previous studies reporting that, even after early introduction of ART treatment, cognitive deficits may persist and can become severe at school age , 40. Theet al. , 42 haveThe authors declare that they have no conflicts of interest. The authors also declare that they have no relevant financial relationships to disclose.MFC was one of the CHER trial’s principal investigators. He and others designed the trial and provided guidance to BL, EMM and AvdK for the design of the CHER‐Plus neuro and imaging sub‐study, within which this study is nested. KvW was responsible for the assessment of English‐ and Afrikaans‐speaking participants, scoring capturing and auditing data, interpreting results, writing up the final manuscript and approving the final manuscript as submitted. She will be the corresponding author and is responsible for submission. BL provided on‐site supervision to KvW throughout this study, gave clinical guidance/care for participants, reviewed the manuscript and suggested relevant changes prior to submission. MFC reviewed the manuscript and made relevant changes prior to submission. MJB provided expert advice for the design of the CHER‐Plus Neuro sub‐study, reviewed the manuscript and made relevant changes prior to submission. EMM and AvdK reviewed the manuscript and made relevant changes prior to submission. MK was responsible for statistical analysis of data, reviewed the manuscript and made relevant changes prior to submission. KGFT provided supervision of this work throughout the entire process, critically reviewed the manuscript and made relevant changes prior to submission. KGFT gave final approval of the manuscript as submitted to the journal. All authors approve the final manuscript as submitted and agree to be accountable for all aspects of the work.


High out‐of‐pocket (OOP) expenditure and inadequate insurance coverage may adversely affect cancer survivors. We aimed to characterize the extent and correlates of healthcare utilization, OOP expenditures, and underinsurance among insured cancer survivors.We used 2011–2015 Medical Expenditure Panel Survey data to identify a nationally representative sample of insured non‐elderly adult (age 18–64 years) cancer survivors. We used negative binomial, two‐part , and logistic regression models to quantify healthcare utilization, OOP expenditures, and underinsurance, respectively, and identified sociodemographic correlates for each outcome.We identified 2738 insured non‐elderly cancer survivors. Adjusted average utilization of ambulatory, non‐ambulatory, prescription medication, and dental services was 14.4, 0.51, 24.9, and 1.4 events per person per year, respectively. Higher ambulatory and dental services utilization were observed in older adults, females, non‐Hispanic Whites, survivors with a college degree and high income, compared to their counterparts. Nearly all (97.7%) survivors had some OOP expenditures, with a mean adjusted OOP expenditure of $1552 per person per year. Adjusted mean OOP expenditures for ambulatory, non‐ambulatory, prescription medication, dental, and other health services were $653, $161, $428, $194, and $83, respectively. Sociodemographic variations in service‐specific OOP expenditures were generally consistent with respective utilization patterns. Overall, 8.8% of the survivors were underinsured.Many insured non‐elderly cancer survivors allocate a substantial portion of their OOP expenditure for healthcare‐related services and experience financial vulnerability, resulting in nearly 8.8% of the survivors being underinsured. Utilization of healthcare services varies across sociodemographic groups. This paper estimates services utilization, out‐of‐pocket (OOP) expenditure, and underinsurance among insured non‐elderly adult cancer survivors. We provide novel evidence on sociodemographic variations in health services utilization and service‐specific OOP expenditure among cancer survivors using Medical Expenditure Panel Survey data. We identified non‐elderly adult (age 18–64 years) cancer survivors using Clinical Classifications Software codes 21–45 from the MC files and linked the information to FYC files. We identified 2738 non‐elderly cancer survivors for whom cancer was reported as a current condition and complete data were available. A medical condition which a respondent was experiencing or had an event linked to during the survey year is defined as a “current condition” in MEPS.2.22.2.1Health services utilization was quantified separately for ambulatory, non‐ambulatory, prescription medication, and dental services. Ambulatory care utilization was the total number of office based and outpatient visits, non‐ambulatory utilization was the total number of inpatient discharges and emergency room (ER) visits, prescription medication utilization was the total number of prescription medication purchase events (including refills), and dental care utilization was the total number of dental visits per person over 1‐year period.2.2.2Total OOP expenditure was the sum of a cancer survivor's expenditure for all health services utilized over 1‐year period.2.2.3Following previous studies, underinsurance was defined using an indicator variable based on the ratio of total OOP and family income (FI). Specifically, it was defined as total OOP ≥5% of FI for FI < 200% federal poverty level (FPL) or ≥10% of FI for FI ≥ 200% FPL, for the individuals having full year insurance coverage.2.2.4Age , sex , race/ethnicity , marital status (not married and married), income level , education , insurance status , number of MEPS priority conditions , self‐reported health status , and census region  were included as covariates in each estimation model. Number of MEPS priority conditions ,2.3We evaluated three outcomes: health services utilization, OOP expenditure, and underinsurance. Health services utilization was estimated using negative binomial model for each service type where the total number of events per person per year was the dependent variable. Total and service‐specific OOPs were estimated using two‐part regression models  with log link and gamma distribution).33.1Our analytic cohort consisted of 2738 non‐elderly cancer survivors. The overall weighted sample was majority White 81.8%), aged 50–59 years (40.9%), female dominant (60.5%), and married (66.8%). Majority of the sample had a college degree or above (39.8%), high income (56%), and private non‐managed care insurance coverage (62.5%). Although 45.6% reported three or more comorbid conditions, nearly half of the sample (48.9%) reported very good or excellent health status  survivors had some OOP expenditure, with adjusted mean OOP per person per year of $1552. Adjusted mean OOP expenditures for ambulatory, non‐ambulatory, prescription medication, dental, and other health services for the full cohort were $653, $161, $428, $194, and $83, respectively. Survivors spent the highest proportion of their total OOP on ambulatory services and the second highest on prescription medications  had significantly higher services utilization, higher OOP expenditures, and increased underinsurance than younger individuals. This is likely related to increased comorbidity, suggesting a need for specific insurance reform for this age group.Black cancer survivors are more likely to receive care from limited resource settings, reducing their access to health services.Prescription medication costs have come under increased scrutiny given the upward trend in cancer therapy pricing.The Affordable Care Act (ACA) made cancer screening more affordable and expanded Medicaid, which resulted in increased preventive services utilization, early‐stage cancer detection, and increased services utilization.We would like to note a few limitations of our study. First, MEPS expenditure and utilization data are patient‐reported, with a sub‐sample cross‐checked with the providers, so there is potential recall bias.In this study, we found many insured non‐elderly cancer survivors have high services utilization and OOP expenditures resulting in nearly 8.8% being underinsured. Lower health services utilization by the underserved cancer survivors indicates that the real extent of the financial hardship may be much worse. This highlights the need to take healthcare access issues into consideration while formulating policies to mitigate financial toxicity. Our study underscores the continued need for further policy changes in health insurance coverage and healthcare access, including among cancer survivors, in the United States.Ethical approval for this study was sought from the Texas A&M University Institutional Review Board and upon initial review they determined the study to be “not research involving human subjects as defined by DHHS and FDA regulations” and “further IRB review and approval by this organization is not required because this is not human research”. Determination date: 01/15/2020. Reference number: 104335.The authors disclose no conflict of interest.Table S1Click here for additional data file.


Pyogenic liver abscess (PLA) is an uncommon yet potentially fatal disease. The disease most commonly arises from biliary infection but may also result from hematogenous seeding and portal spread secondary to bowel contamination, direct seeding, or penetrating trauma. The diagnosis is suspected when there is a hepatic lesion on imaging. Confirmation of diagnosis requires purulent aspirate or bacterial growth on Gram stain/culture of the abscess or blood. The mainstay of treatment is antimicrobials in conjunction with either percutaneous abscess drainage or aspiration. Surgical drainage is reserved for cases of ruptured abscess, peritonitis, or in the presence of an underlying surgical cause. PLA typically presents with fever and abdominal symptoms. We report a case of an unusual manifestation of PLA, presenting as hiccups, which led to a significant delay in the diagnosis and treatment. Pyogenic liver abscess (PLA) is an uncommon yet life‐threatening condition that typically presents with fever, jaundice, and abdominal pain. We report an unusual case of PLA manifesting as hiccups. This case highlights the importance of thoroughly investigating persistent or intractable hiccups for an underlying occult cause including the possibility of a PLA to prevent a delay in the diagnosis and treatment of this highly fatal disease. Pyogenic liver abscess (PLA) is a life‐threatening condition that carries a mortality rate of 2–12%.3, segmented neutrophils 85%, hemoglobin 13.6 g/dL, mean corpuscular volume 84.2 fL, creatinine 1.12 mg/dL, aspartate aminotransferase 17 U/L, alanine aminotransferase 37 U/L, alkaline phosphatase 111 U/L, and total bilirubin 0.7 mg/dL. Contrast‐enhanced, abdominopelvic computed tomography (CT) scan revealed acute sigmoid diverticulitis and diffuse, multiloculated hypodense lesions consistent with liver abscesses, with the largest lesions measuring 8.3 × 5.6 cm and 3.9 × 3.5 cm in size  of 23 400 mmHiccups, also known as singultus, are a common occurrence caused by involuntary, repetitive myoclonic contractions of the diaphragm and intercostal muscles. Hiccups are classified according to their duration: acute (<48 h), persistent (>48 h), and intractable (>1 month). Acute hiccups tend to be self‐limited, transient, and typically do not warrant extensive work‐up. Persistent or intractable hiccups require further investigation for pathology involving the diaphragm, gastrointestinal, cardiopulmonary, or central nervous systems. Medication‐induced and psychogenic causes should also be explored. The most common causes of hiccups include gastroesophageal reflux disease, Parkinson's disease, anxiety, and gastric distension secondary to irritants, i.e. alcohol, tobacco. Serious diseases have been reported to present with hiccups such as pulmonary embolism, myocardial infarction, cerebrovascular accident, subdural hematoma, brain tumor, and intracranial aneurysm.Liver abscesses may pose as an irritant to the diaphragm and be a rare cause of persistent hiccups. There are only two published reports in the literature to our knowledge that describe hiccups as an initial presentation for a PLA.Klebsiella pneumoniae, have been associated with an increased risk of gastrointestinal malignancy.The diagnosis of PLA is established when there is a hepatic lesion on imaging (i.e. ultrasound and CT) in addition to purulent aspirate or bacterial growth on Gram stain/culture of abscess or blood. The mortality of PLA has declined in recent years likely due to improved imaging and diagnostic techniques.Symptomatic treatment of persistent hiccups alone may result in delayed or missed diagnosis, which could be life‐threatening. This case highlights the importance of thoroughly investigating persistent or intractable hiccups for an underlying occult cause including the possibility of a PLA to prevent a delay in the diagnosis and treatment of this highly fatal disease.Written informed consent has been obtained.


RET) is a targetable oncogene. RET fusions have been reported in patients with metastatic colorectal cancer (mCRC). However, RET mutations in mCRC are less studied. Here, we aimed to characterize the clinical, pathological, and molecular landscape of RET‐mutated mCRC.Rearranged during transfection . We compared the clinical, pathological, and molecular characteristics of mCRC cases with tumors that harbored somatic RET mutations  or had wild‐type RET .Five hundred and eighty‐two patients were included in this study. Next‐generation sequencing was performed to detect RET mutations (15/16 [93.8%]) compared to their fraction among cases with wild‐type RET (339/566 [59.9%]). Furthermore, all patients with RET mutations were younger than 60 years (16/16 [100%]), whereas such patients were less predominant in the group with wild‐type RET (379/566 [67.0%]). Individuals with tumors positive for RET mutations more frequently exhibited mucinous histology (5/16 [31.2%] vs. 55/566 [9.7%]), exhibited a lower incidence of liver metastasis (4/16 [25.0%] vs. 335/566 [59.2%]), and higher incidence of peritoneal metastasis (9/16 [56.2%] vs.161/566 [28.4%]), expressed wild‐type TP53 (8/16 [50.0%] vs.120/566 [21.2%]), and showed an increased frequency of MSI‐high (6/16 [37.5%] vs. 18/566 [3.2%]). In those with microsatellite‐stable mCRC, patients with RET mutations had a higher median TMB than patients with wild‐type RET . The median progression‐free survival was similar in individuals with mutated and wild‐type RET on the oxaliplatin‐based regimen .Males comprised the absolute majority of cases with RET mutations represent a separate mCRC subtype. Further studies are needed to evaluate the efficacy of RET inhibitors in mCRC patients with RET mutations.Our study suggests that cases with  RET mutations were enriched in male patients and those younger than 60 years. The first is chromosomal rearrangement, which causes fusion of RET with a partner protein.RET fusions were observed in a small fraction (<1%) of metastatic CRC (mCRC), where their presence was associated with old age, right‐sided tumor origin, wild‐type RAS and BRAF, microsatellite instability (MSI)‐high tumors, and poor prognosis.RET mutations in patients with mCRC are largely unknown. Understanding the clinical impact of RET mutations can help oncologists to properly translate this information into clinical practice. Kato et al. examined a cohort of 300 patients with CRC and identified two individuals with RET mutations.RET mutations precluded their further analysis. Hence, this study aimed to evaluate the frequency and phenotypic characteristics of mCRC with somatic RET mutation in a larger patient cohort.There are mainly two mechanisms underlying 2The following patients were selected: (1) those diagnosed with mCRC based on pathology samples at Sun Yat‐sen University Cancer between 1 January 2015 and 31 March 2020; (2) those who had an available record of the next‐generation sequencing result performed at our institute. Those who (1) accepted chemotherapy or radiotherapy before next‐generation sequencing analysis or (2) had no available follow‐up information were excluded. The study was performed in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Sun Yat‐sen University Cancer (GZKJ2020‐02), and all patients provided informed consent. The detailed methods for next‐generation sequencing and tumor mutation burden (TMB) calculation are reported in a previously published paperRET mutations and the following variables: age, gender, primary tumor location, differentiation, mucinous histology, T stage, N stage, time to metastasis, metastatic organs, TP53 mutations, APC mutation, RAS mutations, BRAF mutations, SMAD4 mutations, PIK3CA mutations, ERBB2 amplifications, and MSI status. Patients’ characteristics were compared by using the Chi‐squared test. TMBs were compared by using the Kruskal–Wallis test. Kaplan–Meier survival analysis and log‐rank test were performed to detect differences in progression‐free survival (PFS). The statistical tests were two‐tailed and the differences were considered statistically significant when p < 0.05.We evaluated the association between the presence of somatic 3RET mutations were identified in 16 (2.7%) patients. The genomic alterations of RET‐mutated tumors are shown in Table Five hundred and eighty‐two patients were included in this study Figure . The pated in 16 .7% patieRET mutations was associated with male sex (15/16 [93.8%] patients with mutated RET vs. 339/566 [59.9%] patients with wild‐type RET), younger age , mucinous histology (5/16 [31.2%] patients with mutated RET vs. 55/566 [9.7%] patients with wild‐type RET), less liver metastasis (4/16 [25.0%] patients with mutated RET vs. 335/566 [59.2%] patients with wild‐type RET), and more peritoneal metastasis . No Lynch syndrome was observed in patients with RET mutation. There were no significant associations between RET mutation presence and APC, RAS, BRAF, SMAD4, PIK3CA, or ERBB2 status. In patients with microsatellite‐stable mCRC, those with RET mutations had a higher TMB than those with wild‐type RET . In patients with MSI‐high mCRC, those with RET mutations had a TMB similar to that of patients with wild‐type RET .As shown in Table RET mutations and those with wild‐type RET  were administered oxaliplatin‐based first‐line chemotherapy (FOLFOX or XELOX), whereas 63 (10.8%) received irinotecan‐based chemotherapy (FOLFIRI or XELIRI). Among the mCRC patients on the oxaliplatin‐based regimen, PFS was similar between patients with 4RET mutations in 16 (2.7%) individuals. Furthermore, RET mutations were more often present in younger male patients and they were associated more strongly with mucinous histology, weaker liver metastasis, and stronger peritoneal metastasis. Given that the incidence of RET mutations in mCRC patients is low, our study provides valuable information to potentially help physicians identify the enriched subgroups in which RET mutations could be more frequent.In our analysis of 582 patients with mCRC, we detected RET aberrations in 4,871 patients with diverse malignancies.RET mutations. The tumor stages were unknown, and this might explain the low mutation rate of RET in that study. Villava et al. analyzed 197 patients and identified four patients with colorectal cancer and RET mutation. Oliveira et al. also identified four different somatic RET mutations in patients with colon cancer. However, the rare occurrence of RET mutations precluded further analysis in those studies. Recently, Zhao et al. retrospectively reviewed 3,272 patients with CRC and documented that the RET mutation rate was 3.39%,RET fusions have been observed in a small subgroup of patients with mCRC. RET fusions were associated with older age, origin on the right side, wild‐type RAS and BRAF, MSI‐high tumors, as well as shorter survival.RET mutations and patients’ characteristics . These additional information revealed in our study suggests a potential enrichment strategy for further trials with targeted agents focused on the RET mutation. Second, we found RET mutations were enriched in younger and male patients. We did not observe significant correlations between the presence of RET mutations and primary tumor location and RAS or BRAF status, on the other hand. These results suggest that factors affecting the incidence of RET mutations are different from those associated with RET rearrangements, and RET‐mutated tumors did not share with RET fusions the same clinical and pathological characteristics. In our study, MSI‐high was associated with RET mutation. This may be caused by genome instability in MSI‐high tumors. Further studies are needed to evaluate the impact of RET mutation on response to immunotherapy in MSI‐high tumors.Kato et al. studied RET mutations and fusions in vivo, and it showed durable clinical responses in patients with RET‐altered NSCLC.RET‐fused NSCLC and RET‐fused or mutated MTC.RET‐mutated mCRC were included in a clinical trial (NCT03037385), and the efficacy of pralsetinib is still under evaluation in these two patients.To date, there are several commercially available inhibitors demonstrating activity against RET. Most of them are nonspecific inhibitors, such as cabozantinib, vandetanib, lenvatinib, sorafenib, sunitinib, and alectinib.RET mutations in this study could be driver aberrations or only neutral passengers. We could not distinguish between them owing to limited information regarding RET mutations in mCRC. Furthermore, because the follow‐up time was limited, overall survival could not be determined.Our study had few limitations, such as a limited sample size and retrospective bias. The identified 5RET mutation in 2.7% patients with mCRC, and showed males younger than 60 years of age comprised the absolute majority of patients with RET mutations. These observations suggest that tumors with RET mutation represent a novel subtype of mCRC, and are different from tumors with RET fusion. Further studies are needed to evaluate the efficacy of RET inhibitors in patients with RET‐mutated mCRC.Despite these limitations, our study noted All patients signed informed consent. The study was performed in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Sun Yat‐sen University Cancer Center (GZKJ2020‐02).The authors declare that they have no competing interest.Concept and design: All authors. Acquisition, analysis, or interpretation of data: YZ Yang and WM Hu. Drafting of the manuscript: WZ He and LP Xia. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: WZ He, WM Hu, and LP Xia. Obtained funding: WZ He. Administrative, technical, or material support: YZ Yang and WM Hu. Supervision: LP Xia.Supplementary MaterialClick here for additional data file.


As the numerous clinical parameters show multicollinearity between each other, we performed a multiple regression analysis to consider this fact. Respiratory distress syndrome is significantly associated with gestational age at PPROM (p < 0.001), and surfactant application is significantly associated with PPROM duration (p = 0.014). The other respiratory parameters including steroids and diuretics therapy, bronchopulmonary dysplasia, and the neurological parameters  were not significantly associated with PPROM duration or gestational age at PPROM diagnosis.In preterm premature rupture of membranes (PPROM), a decision between early delivery with prematurity complications and pregnancy prolongation bearing the risk of chorioamnionitis has to be made. To define disadvantages of delayed prolongation, latency duration of PPROM in expectantly managed pregnancies was investigated. We included those PPROMs > 48 h leading to preterm birth prior 37 weeks’ gestation and retrospectively analyzed 84 preterm infants fulfilling these criteria. The association between latency duration/appearance of PPROM and respiratory outcome (primary outcomes) and neurological outcome (secondary outcomes) was investigated. The study showed that latency duration of PPROM is not associated with clinical or histological chorioamnionitis (Conclusion: Latency duration of PPROM was not associated with adverse neonatal outcome in expectantly and carefully managed pregnancies, but respiratory distress syndrome was pronounced. The observed effect of pronounced respiratory distress syndrome can be treated with surfactant preparations and was not followed by increased rate of bronchopulmonary dysplasia.The online version contains supplementary material available at 10.1007/s00431-021-04245-2. Preterm premature rupture of membranes (PPROM), defined as premature rupture of membranes occurring before 37 weeks of gestation, is a serious complication during pregnancy. PPROM enables ascending infections from the vagina of pregnant women and conceivably leads to chorioamnionitis, a dangerous situation for mother and fetus. Intra-amniotic inflammation is observed in approximately 40% of women with PPROM . FurtherWe retrospectively analyzed 84 preterm infants born after PPROM and delivered between 2005 and 2014 at the University Hospital Essen. We included those PPROMs leading to preterm birth prior 37 weeks’ gestation and Fig. Gestational age at birth was defined as time elapsed between the first day of the last menstrual period and the day of delivery and was confirmed by ultrasound. The clinical definition of chorioamnionitis included maternal fever (temperature ≥ 38.0 °C) with at least one of the following clinical signs: abdominal tenderness, fetal tachycardia (continuous heart rate above 160/minute), maternal biochemical evidence of chorioamnionitis  of at least 2.0 mg/dl, and/or leukocytosis ) or purulent vaginal discharge . FurtherWe defined parameter of the respiratory outcome as primary outcomes, and we decided to evaluate several binary factors: respiratory distress syndrome, surfactant application, bronchopulmonary dysplasia, therapy with continuous positive airway pressure (CPAP), and therapy with diuretics and steroids. As secondary outcomes, we regarded the neurological parameters intraventricular hemorrhage and Bayley II testing at a corrected age of 24 months.Respiratory distress syndrome was defined by X-ray in any child with respiratory insufficiency according to Giedion et al. . We alsoThe latency duration as well as appearance  of PPROM were correlated to different clinical parameters of primary and secondary outcomes and in particular to the frequency of chorioamnionitis.t test if data are approximately normally distributed. For skewed distributions Mann-Whitney U test has been used instead. Ordinally scaled parameters  have been compared with Cochran-Armitage trend test. In order to compare relative frequencies, chi2 test or Fisher’s exact test has been performed. In order to quantify the strength of correlation of two quantitative variables, correlation coefficients according to Pearson were assessed. If one or both variables were ordinally scaled, coefficients according to Spearman were calculated instead. Furthermore, multiple logistic regression analysis was performed for the binary outcomes: respiratory distress syndrome, surfactant application, bronchopulmonary dysplasia, therapy with continuous positive airway pressure, therapy with diuretics, and therapy with steroids in order to investigate the influence of several parameters on the outcome simultaneously. For the multiple models, all variables have been considered which had been proven to be significant at a level of α = 0.10 in the univariable analyses. For the final model, variables had been selected using the “selection = stepwise” option. The goodness of these models has been quantified by the AUC (area under the curve) value. In general, test results with p values less than 0.05 have been regarded as statistically significant.Statistical analysis was performed with SAS software, release 9.4 . Quantitative variables are presented by mean value and standard deviation together with median, minimum, and maximum. For qualitative factors, absolute and relative frequencies are given. Mean values of two independent groups have been compared with a two sample Tables p = 0.332; Mann-Whitney U test). Additionally, we examined the presence of clinically defined chorioamnionitis .Histological evaluation of the placenta was performed in 58 out of 84 women (69%). In 2009, pathologic examination of placentas in the case of suspected chorioamnionitis was introduced leading to increased frequency of appropriate placental pathological examinations. Histological chorioamnionitis was diagnosed in 13 out of 58 examined placentas , maximal grade of respiratory distress syndrome (p = 0.010), and number of surfactant applications (p = 0.001). Multiple logistic regression analysis showed that only gestational age of occurrence of PPROM is significant. However, when using a multiple regression analysis with the quantitative outcome “duration of PPROM” besides the most important parameter gestational age at PPROM (p < 0.001), maximal grade of respiratory distress syndrome (p = 0.047) as well as number of surfactant applications (p = 0.049) revealed to be significant.Additionally, patients were divided into two groups  and Klebsiella pneumonia (one infant). In three of the 10 corresponding mothers bacteria and/or candida were detected; the vaginal colonization of one mother included E. coli and Candida glabrata. In only two of these 10 infants (20%), bacteria detected in neonatal infection corresponded to the vaginal colonization of the mothers.Early-onset sepsis was diagnosed in 10 infants. Table p < 0.001; r = 0.397) and respiratory distress syndrome . The other respiratory parameters , therapy with continuous positive airway pressure, therapy with diuretics, therapy with steroids) as well as neonatal parameters  showed no correlation with PPROM duration . The respiratory parameters bronchopulmonary dysplasia , therapy with continuous positive airway pressure, therapy with diuretics, therapy with steroids, and the additional neonatal parameters  showed no significant correlation with gestational age at diagnosis of PPROM (each p > 0.05).First, correlation analysis between duration of PPROM and respiratory parameters as well as between gestational age at diagnosis of PPROM and respiratory parameters was performed using univariable analysis. We found significant correlations between duration of PPROM and the following parameters: surfactant application (p < 0.001), and surfactant application is significantly associated with PPROM duration and birth weight (p = 0.014 and p = 0.001). These results are confirmed by Table As the numerous clinical parameters show multicollinearity between each other, we performed a multiple regression analysis using the “selection = stepwise” option for each outcome to consider this fact. Table p < 0.05). Nevertheless, the AUC values ranging between 0.755 and 0.830 indicate a rather good statistical modelling.For all outcomes in Table p = 0.737). Additionally, the occurrence of bronchopulmonary dysplasia or the bronchopulmonary dysplasia therapy (therapy with diuretics or steroids) was not significantly correlated with PPROM duration or gestational age at PPROM using regression analysis (Table p = 0.058). The rate of bronchopulmonary dysplasia in infants born after PPROM duration < 7 days in comparison to infants born after PPROM ≥ 7 days was not different .The neurological parameters , PDI score (Bayley test)) showed no correlation with PPROM duration nor with gestational age at diagnosis of PPROM . The 32 tested infants revealed an MDI score of 95.4 ± 18.0 . In 25 of the 32 tested infants, a PDI score was determined 86.6 ± 16.0 . Additionally, infants born after PPROM duration < 7 days showed no significantly different neurodevelopmental outcome at a corrected age of 24 months from infants born after PPROM ≥ 7 days when determining MDI and PDI scores  was observed in infants born between 27 and 32 weeks of gestation [In contrast to the results of this study, Pharande et al. reported about a higher incidence of chronic lung disease/mortality in infants with PPROM prior to 24 weeks’ gestation, but a lower incidence of chronic lung disease/mortality in infants with PPROM at or after 24 weeks in comparison to infants without PPROM . Furtherestation . Developestation . The appestation , 20. Intestation .Ureaplasma urealyticum and Mycoplasma hominis), Gardnerella vaginalis and Bacteroides, Group B streptococcus, and gram-negative bacteria such as Escherichia coli which are part of the vaginal or enteric flora [E. coli in infants with early-onset sepsis and in the corresponding vaginal colonization of their mothers Below is the link to the electronic supplementary material.