Inflammatory exposure drives long-lived impairment of hematopoietic stem cell self-renewal activity and accelerated aging.

Hematopoietic stem cells (HSCs) mediate regeneration of the hematopoietic system following injury, such as following infection or inflammation. These challenges impair HSC function, but whether this functional impairment extends beyond the duration of inflammatory exposure is unknown. Unexpectedly, we observed an irreversible depletion of functional HSCs following challenge with inflammation or bacterial infection, with no evidence of any recovery up to 1 year afterward. HSCs from challenged mice demonstrated multiple cellular and molecular features of accelerated aging and developed clinically relevant blood and bone marrow phenotypes not normally observed in aged laboratory mice but commonly seen in elderly humans. In vivo HSC self-renewal divisions were absent or extremely rare during both challenge and recovery periods. The progressive, irreversible attrition of HSC function demonstrates that temporally discrete inflammatory events elicit a cumulative inhibitory effect on HSCs. This work positions early/mid-life inflammation as a mediator of lifelong defects in tissue maintenance and regeneration.

Single cell analyses identify a highly regenerative and homogenous human CD34+ hematopoietic stem cell population.

The heterogeneous nature of human CD34+ hematopoietic stem cells (HSCs) has hampered our understanding of the cellular and molecular trajectories that HSCs navigate during lineage commitment. Using various platforms including single cell RNA-sequencing and extensive xenotransplantation, we have uncovered an uncharacterized human CD34+ HSC population. These CD34+EPCR+(CD38/CD45RA)- (simply as EPCR+) HSCs have a high repopulating and self-renewal abilities, reaching a stem cell frequency of ~1 in 3 cells, the highest described to date. Their unique transcriptomic wiring in which many gene modules associated with differentiated cell lineages confers their multilineage lineage output both in vivo and in vitro. At the single cell level, EPCR+ HSCs are the most transcriptomically and functionally homogenous human HSC population defined to date and can also be easily identified in post-natal tissues. Therefore, this EPCR+ population not only offers a high human HSC resolution but also a well-structured human hematopoietic hierarchical organization at the most primitive level.

The proteogenomic subtypes of acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive blood cancer with a poor prognosis. We report a comprehensive proteogenomic analysis of bone marrow biopsies from 252 uniformly treated AML patients to elucidate the molecular pathophysiology of AML in order to inform future diagnostic and therapeutic approaches. In addition to in-depth quantitative proteomics, our analysis includes cytogenetic profiling and DNA/RNA sequencing. We identify five proteomic AML subtypes, each reflecting specific biological features spanning genomic boundaries. Two of these proteomic subtypes correlate with patient outcome, but none is exclusively associated with specific genomic aberrations. Remarkably, one subtype (Mito-AML), which is captured only in the proteome, is characterized by high expression of mitochondrial proteins and confers poor outcome, with reduced remission rate and shorter overall survival on treatment with intensive induction chemotherapy. Functional analyses reveal that Mito-AML is metabolically wired toward stronger complex I-dependent respiration and is more responsive to treatment with the BCL2 inhibitor venetoclax.

Inflammatory fibroblasts mediate resistance to neoadjuvant therapy in rectal cancer.

Standard cancer therapy targets tumor cells without considering possible damage on the tumor microenvironment that could impair therapy response. In rectal cancer patients we find that inflammatory cancer-associated fibroblasts (iCAFs) are associated with poor chemoradiotherapy response. Employing a murine rectal cancer model or patient-derived tumor organoids and primary stroma cells, we show that, upon irradiation, interleukin-1α (IL-1α) not only polarizes cancer-associated fibroblasts toward the inflammatory phenotype but also triggers oxidative DNA damage, thereby predisposing iCAFs to p53-mediated therapy-induced senescence, which in turn results in chemoradiotherapy resistance and disease progression. Consistently, IL-1 inhibition, prevention of iCAFs senescence, or senolytic therapy sensitizes mice to irradiation, while lower IL-1 receptor antagonist serum levels in rectal patients correlate with poor prognosis. Collectively, we unravel a critical role for iCAFs in rectal cancer therapy resistance and identify IL-1 signaling as an attractive target for stroma-repolarization and prevention of cancer-associated fibroblasts senescence.

Ano-rectal wall dose-surface maps localize the dosimetric benefit of hydrogel rectum spacers in prostate cancer radiotherapy.

BACKGROUND AND PURPOSE: To evaluate spatial differences in dose distributions of the ano-rectal wall (ARW) using dose-surface maps (DSMs) between prostate cancer patients receiving intensity-modulated radiation therapy with and without implantable rectum spacer (IMRT+IRS; IMRT-IRS, respectively), and to correlate this with late gastro-intestinal (GI) toxicities using validated spatial and non-spatial normal-tissue complication probability (NTCP) models. MATERIALS AND METHODS: For 26 patients DSMs of the ARW were generated. From the DSMs various shape-based dose measures were calculated at different dose levels: lateral extent, longitudinal extent, and eccentricity. The contiguity of the ARW dose distribution was assessed by the contiguous-DSH (cDSH). Predicted complication rates between IMRT+IRS and IMRT-IRS plans were assessed using a spatial NTCP model and compared against a non-spatial NTCP model. RESULTS: Dose surface maps are generated for prostate radiotherapy using an IRS. Lateral extent, longitudinal extent and cDSH were significantly lower in IMRT+IRS than for IMRT-IRS at high-dose levels. Largest significant differences were observed for cDSH at dose levels >50 Gy, followed by lateral extent at doses >57 Gy, and longitudinal extent in anterior and superior-inferior directions. Significant decreases (p = 0.01) in median rectal and anal NTCPs (respectively, Gr 2 late rectal bleeding and subjective sphincter control) were predicted when using an IRS. CONCLUSIONS: Local-dose effects are predicted to be significantly reduced by an IRS. The spatial NTCP model predicts a significant decrease in Gr 2 late rectal bleeding and subjective sphincter control. Dose constraints can be improved for current clinical treatment planning.

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia.

PURPOSE: The purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands. MATERIAL AND METHODS: A cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0-6 months (early), 6-15 months (late), 15-24 months (long-term), and at any time (a longitudinal model) after radiotherapy. Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC) of univariate logistic regression models. The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively. We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post hoc analysis. RESULTS: NTCP models based on the parotid mean dose failed to predict xerostomia (AUCs < 0.60). The most informative predictors were found for late and long-term xerostomia. Late xerostomia correlated with the contralateral dose gradient in the anterior-posterior (AUC = 0.72) and the right-left (AUC = 0.68) direction, whereas long-term xerostomia was associated with parotid volumes (AUCs > 0.85), dose gradients in the right-left (AUCs > 0.78), and the anterior-posterior (AUCs > 0.72) direction. Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose-volume histogram (DVH) spread with the generalization AUCs ranging from 0.74 to 0.88. On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection. We found no advantage in using data cleaning or class balancing methods. CONCLUSION: We demonstrated that incorporation of organ- and dose-shape descriptors is beneficial for xerostomia prediction in highly conformal radiotherapy treatments. Due to strong reliance on patient-specific, dose-independent factors, our results underscore the need for development of personalized data-driven risk profiles for NTCP models of xerostomia. The facilitated machine learning pipeline is described in detail and can serve as a valuable reference for future work in radiomic and dosiomic NTCP modeling.

Vitamin A-Retinoic Acid Signaling Regulates Hematopoietic Stem Cell Dormancy.

Dormant hematopoietic stem cells (dHSCs) are atop the hematopoietic hierarchy. The molecular identity of dHSCs and the mechanisms regulating their maintenance or exit from dormancy remain uncertain. Here, we use single-cell RNA sequencing (RNA-seq) analysis to show that the transition from dormancy toward cell-cycle entry is a continuous developmental path associated with upregulation of biosynthetic processes rather than a stepwise progression. In addition, low Myc levels and high expression of a retinoic acid program are characteristic for dHSCs. To follow the behavior of dHSCs in situ, a Gprc5c-controlled reporter mouse was established. Treatment with all-trans retinoic acid antagonizes stress-induced activation of dHSCs by restricting protein translation and levels of reactive oxygen species (ROS) and Myc. Mice maintained on a vitamin A-free diet lose HSCs and show a disrupted re-entry into dormancy after exposure to inflammatory stress stimuli. Our results highlight the impact of dietary vitamin A on the regulation of cell-cycle-mediated stem cell plasticity. VIDEO ABSTRACT.

Parotid gland mean dose as a xerostomia predictor in low-dose domains.

PURPOSE: Xerostomia is a common side effect of radiotherapy resulting from excessive irradiation of salivary glands. Typically, xerostomia is modeled by the mean dose-response characteristic of parotid glands and prevented by mean dose constraints to either contralateral or both parotid glands. The aim of this study was to investigate whether normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands are suitable for the prediction of xerostomia in a highly conformal low-dose regime of modern intensity-modulated radiotherapy (IMRT) techniques. MATERIAL AND METHODS: We present a retrospective analysis of 153 head and neck cancer patients treated with radiotherapy. The Lyman Kutcher Burman (LKB) model was used to evaluate predictive power of the parotid gland mean dose with respect to xerostomia at 6 and 12 months after the treatment. The predictive performance of the model was evaluated by receiver operating characteristic (ROC) curves and precision-recall (PR) curves. RESULTS: Average mean doses to ipsilateral and contralateral parotid glands were 25.4 Gy and 18.7 Gy, respectively. QUANTEC constraints were met in 74% of patients. Mild to severe (G1+) xerostomia prevalence at both 6 and 12 months was 67%. Moderate to severe (G2+) xerostomia prevalence at 6 and 12 months was 20% and 15%, respectively. G1 + xerostomia was predicted reasonably well with area under the ROC curve ranging from 0.69 to 0.76. The LKB model failed to provide reliable G2 + xerostomia predictions at both time points. CONCLUSIONS: Reduction of the mean dose to parotid glands below QUANTEC guidelines resulted in low G2 + xerostomia rates. In this dose domain, the mean dose models predicted G1 + xerostomia fairly well, however, failed to recognize patients at risk of G2 + xerostomia. There is a need for the development of more flexible models able to capture complexity of dose response in this dose regime.

Prospective identification of hematopoietic lineage choice by deep learning.

Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoietic progenitors using image patches from brightfield microscopy and cellular movement. Surprisingly, lineage choice can be detected up to three generations before conventional molecular markers are observable. Our approach allows identification of cells with differentially expressed lineage-specifying genes without molecular labeling.

Unbiased Prediction and Feature Selection in High-Dimensional Survival Regression.

With widespread availability of omics profiling techniques, the analysis and interpretation of high-dimensional omics data, for example, for biomarkers, is becoming an increasingly important part of clinical medicine because such datasets constitute a promising resource for predicting survival outcomes. However, early experience has shown that biomarkers often generalize poorly. Thus, it is crucial that models are not overfitted and give accurate results with new data. In addition, reliable detection of multivariate biomarkers with high predictive power (feature selection) is of particular interest in clinical settings. We present an approach that addresses both aspects in high-dimensional survival models. Within a nested cross-validation (CV), we fit a survival model, evaluate a dataset in an unbiased fashion, and select features with the best predictive power by applying a weighted combination of CV runs. We evaluate our approach using simulated toy data, as well as three breast cancer datasets, to predict the survival of breast cancer patients after treatment. In all datasets, we achieve more reliable estimation of predictive power for unseen cases and better predictive performance compared to the standard CoxLasso model. Taken together, we present a comprehensive and flexible framework for survival models, including performance estimation, final feature selection, and final model construction. The proposed algorithm is implemented in an open source R package (SurvRank) available on CRAN.

Single-cell transcriptomic reconstruction reveals cell cycle and multi-lineage differentiation defects in Bcl11a-deficient hematopoietic stem cells.

BACKGROUND: Hematopoietic stem cells (HSCs) are a rare cell type with the ability of long-term self-renewal and multipotency to reconstitute all blood lineages. HSCs are typically purified from the bone marrow using cell surface markers. Recent studies have identified significant cellular heterogeneities in the HSC compartment with subsets of HSCs displaying lineage bias. We previously discovered that the transcription factor Bcl11a has critical functions in the lymphoid development of the HSC compartment. RESULTS: In this report, we employ single-cell transcriptomic analysis to dissect the molecular heterogeneities in HSCs. We profile the transcriptomes of 180 highly purified HSCs (Bcl11a (+/+) and Bcl11a (-/-)). Detailed analysis of the RNA-seq data identifies cell cycle activity as the major source of transcriptomic variation in the HSC compartment, which allows reconstruction of HSC cell cycle progression in silico. Single-cell RNA-seq profiling of Bcl11a (-/-) HSCs reveals abnormal proliferative phenotypes. Analysis of lineage gene expression suggests that the Bcl11a (-/-) HSCs are constituted of two distinct myeloerythroid-restricted subpopulations. Remarkably, similar myeloid-restricted cells could also be detected in the wild-type HSC compartment, suggesting selective elimination of lymphoid-competent HSCs after Bcl11a deletion. These defects are experimentally validated in serial transplantation experiments where Bcl11a (-/-) HSCs are myeloerythroid-restricted and defective in self-renewal. CONCLUSIONS: Our study demonstrates the power of single-cell transcriptomics in dissecting cellular process and lineage heterogeneities in stem cell compartments, and further reveals the molecular and cellular defects in the Bcl11a-deficient HSC compartment.

Computational assignment of cell-cycle stage from single-cell transcriptome data.

The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering dividing populations of cells, is to understand the cell-cycle stage of each captured cell. Here we describe and compare five established supervised machine learning methods and a custom-built predictor for allocating cells to their cell-cycle stage on the basis of their transcriptome. In particular, we assess the impact of different normalisation strategies and the usage of prior knowledge on the predictive power of the classifiers. We tested the methods on previously published datasets and found that a PCA-based approach and the custom predictor performed best. Moreover, our analysis shows that the performance depends strongly on normalisation and the usage of prior knowledge. Only by leveraging prior knowledge in form of cell-cycle annotated genes and by preprocessing the data using a rank-based normalisation, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols.

Diffusion maps for high-dimensional single-cell analysis of differentiation data.

MOTIVATION: Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popular multivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages. RESULTS: Here, we propose the use of diffusion maps to deal with the problem of defining differentiation trajectories. We adapt this method to single-cell data by adequate choice of kernel width and inclusion of uncertainties or missing measurement values, which enables the establishment of a pseudotemporal ordering of single cells in a high-dimensional gene expression space. We expect this output to reflect cell differentiation trajectories, where the data originates from intrinsic diffusion-like dynamics. Starting from a pluripotent stage, cells move smoothly within the transcriptional landscape towards more differentiated states with some stochasticity along their path. We demonstrate the robustness of our method with respect to extrinsic noise (e.g. measurement noise) and sampling density heterogeneities on simulated toy data as well as two single-cell quantitative polymerase chain reaction datasets (i.e. mouse haematopoietic stem cells and mouse embryonic stem cells) and an RNA-Seq data of human pre-implantation embryos. We show that diffusion maps perform considerably better than Principal Component Analysis and are advantageous over other techniques for non-linear dimension reduction such as t-distributed Stochastic Neighbour Embedding for preserving the global structures and pseudotemporal ordering of cells. AVAILABILITY AND IMPLEMENTATION: The Matlab implementation of diffusion maps for single-cell data is available at https://www.helmholtz-muenchen.de/icb/single-cell-diffusion-map. CONTACT: fbuettner.phys@gmail.com, fabian.theis@helmholtz-muenchen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combined Single-Cell Functional and Gene Expression Analysis Resolves Heterogeneity within Stem Cell Populations.

Heterogeneity within the self-renewal durability of adult hematopoietic stem cells (HSCs) challenges our understanding of the molecular framework underlying HSC function. Gene expression studies have been hampered by the presence of multiple HSC subtypes and contaminating non-HSCs in bulk HSC populations. To gain deeper insight into the gene expression program of murine HSCs, we combined single-cell functional assays with flow cytometric index sorting and single-cell gene expression assays. Through bioinformatic integration of these datasets, we designed an unbiased sorting strategy that separates non-HSCs away from HSCs, and single-cell transplantation experiments using the enriched population were combined with RNA-seq data to identify key molecules that associate with long-term durable self-renewal, producing a single-cell molecular dataset that is linked to functional stem cell activity. Finally, we demonstrated the broader applicability of this approach for linking key molecules with defined cellular functions in another stem cell system.

Decoding the regulatory network of early blood development from single-cell gene expression measurements.

Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.

Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis.

Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterized by distinctive transcription factor expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated transcription factor pairings, including previously unrecognized relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single-cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.

The dose-response of the anal sphincter region--an analysis of data from the MRC RT01 trial.

PURPOSE: Most studies investigating the dose-response of the rectum focus on rectal bleeding. However, it has been reported that other symptoms such as urgency or sphincter control have a large impact on quality-of-life and that different symptoms are related to the dose to different parts of the anorectal wall. In this study correlations between the 3D dose distribution to the anal-sphincter region and radiation-induced side-effects were quantified. MATERIALS AND METHODS: Dose-surface maps of the anal canal were generated. Next, longitudinal and lateral extent and eccentricity were calculated at different dose levels; DSHs and DVHs were also determined. Correlations between these dosimetric measures and seven clinically relevant endpoints were determined by assessing dosimetric constraints. Furthermore, an LKB model was generated. The study was performed using the data of 388 prostate patients from the RT01 trial (ISRCTN 47772397). RESULTS: Subjective sphincter control was significantly correlated with the dose to the anal surface. The strongest correlations were found for lateral extent at 53 Gy (p=0.01). Outcome was also significantly correlated with the DSH and the mean dose to the anal surface. CONCLUSIONS: The dose to the anal sphincter region should be taken into account when generating treatment-plans. This could be done using shape-based tools, DSH/DVH-based tools or an NTCP model.

Novel approaches to improve the therapeutic index of head and neck radiotherapy: an analysis of data from the PARSPORT randomised phase III trial.

PURPOSE: Subjective xerostomia is a common side-effect following radiotherapy for the treatment of head-and-neck cancer. Standard mean dose models previously used to model xerostomia only that partially predict the occurrence of xerostomia. Studies in animal models have suggested that there are regional variations in the radiosensitivity of the parotid glands. In this work we tested the hypothesis that this is also true for the human parotid gland. METHODS: We present novel dose-response models explicitly taking the spatial distribution of the radiation dose into account. We considered dose to the submandibular gland and other clinical factors and used a variable-selection algorithm to select the best dose-response model. This methodology was applied to 63 head and neck cancer patients and validated using two independent patient cohorts of 19 and 29 patients, respectively. RESULTS: The predictive accuracy of dose-response models improved significantly when including regional variations of radiosensitivity of the parotid glands compared to standard mean-dose models (p = 0.001, t-test). Beneficial dose-pattern analysis demonstrated the importance of minimising dose to the lateral and cranial component of the human parotid gland in order to avoid xerostomia. Furthermore we found an evidence that surgical removal of the sub-mandibular gland significantly increases the risk of radiation-induced xerostomia. CONCLUSION: Dose-response models which take the shape of the dose-distribution into account predicted xerostomia significantly better than standard mean-dose models. Our novel model could be used to rank potential treatment plans more reliably according to their therapeutic index and may be useful to generate better treatment plans.

Assessing correlations between the spatial distribution of the dose to the rectal wall and late rectal toxicity after prostate radiotherapy: an analysis of data from the MRC RT01 trial (ISRCTN 47772397).

Many studies have been performed to assess correlations between measures derived from dose-volume histograms and late rectal toxicities for radiotherapy of prostate cancer. The purpose of this study was to quantify correlations between measures describing the shape and location of the dose distribution and different outcomes. The dose to the rectal wall was projected on a two-dimensional map. In order to characterize the dose distribution, its centre of mass, longitudinal and lateral extent, and eccentricity were calculated at different dose levels. Furthermore, the dose-surface histogram (DSH) was determined. Correlations between these measures and seven clinically relevant rectal-toxicity endpoints were quantified by maximally selected standardized Wilcoxon rank statistics. The analysis was performed using data from the RT01 prostate radiotherapy trial. For some endpoints, the shape of the dose distribution is more strongly correlated with the outcome than simple DSHs. Rectal bleeding was most strongly correlated with the lateral extent of the dose distribution. For loose stools, the strongest correlations were found for longitudinal extent; proctitis was most strongly correlated with DSH. For the other endpoints no statistically significant correlations could be found. The strengths of the correlations between the shape of the dose distribution and outcome differed considerably between the different endpoints. Due to these significant correlations, it is desirable to use shape-based tools in order to assess the quality of a dose distribution.