Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

ß-Catenin Drives Butyrophilin-like Molecule Loss and γδ T-cell Exclusion in Colon Cancer.

Intraepithelial lymphocytes (IEL) expressing γδ T-cell receptors (γδTCR) play key roles in elimination of colon cancer. However, the precise mechanisms by which progressing cancer cells evade immunosurveillance by these innate T cells are unknown. Here, we investigated how loss of the Apc tumor suppressor in gut tissue could enable nascent cancer cells to escape immunosurveillance by cytotoxic γδIELs. In contrast with healthy intestinal or colonic tissue, we found that γδIELs were largely absent from the microenvironment of both mouse and human tumors, and that butyrophilin-like (BTNL) molecules, which can critically regulate γδIEL through direct γδTCR interactions, were also downregulated in tumors. We then demonstrated that ß-catenin activation through loss of Apc rapidly suppressed expression of the mRNA encoding the HNF4A and HNF4G transcription factors, preventing their binding to promoter regions of Btnl genes. Reexpression of BTNL1 and BTNL6 in cancer cells increased γδIEL survival and activation in coculture assays but failed to augment their cancer-killing ability in vitro or their recruitment to orthotopic tumors. However, inhibition of ß-catenin signaling via genetic deletion of Bcl9/Bcl9L in either Apc-deficient or mutant ß-catenin mouse models restored Hnf4a, Hnf4g, and Btnl gene expression and γδ T-cell infiltration into tumors. These observations highlight an immune-evasion mechanism specific to WNT-driven colon cancer cells that disrupts γδIEL immunosurveillance and furthers cancer progression.

MmCMS: mouse models' consensus molecular subtypes of colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue. METHODS: Using transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n = 577) and mouse (n = 57 across n = 8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers. RESULTS: We developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours. CONCLUSIONS: When applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package enables researchers to select suitable mouse models of human CRC subtype for their experimental testing.

Biological Misinterpretation of Transcriptional Signatures in Tumor Samples Can Unknowingly Undermine Mechanistic Understanding and Faithful Alignment with Preclinical Data.

PURPOSE: Precise mechanism-based gene expression signatures (GES) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signaling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling. EXPERIMENTAL DESIGN: Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. RESULTS: Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment. CONCLUSIONS: Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.

Activation of innate-adaptive immune machinery by poly(I:C) exposes a therapeutic vulnerability to prevent relapse in stroma-rich colon cancer.

OBJECTIVE: Stroma-rich tumours represent a poor prognostic subtype in stage II/III colon cancer (CC), with high relapse rates and limited response to standard adjuvant chemotherapy. DESIGN: To address the lack of efficacious therapeutic options for patients with stroma-rich CC, we stratified our human tumour cohorts according to stromal content, enabling identification of the biology underpinning relapse and potential therapeutic vulnerabilities specifically within stroma-rich tumours that could be exploited clinically. Following human tumour-based discovery and independent clinical validation, we use a series of in vitro and stroma-rich in vivo models to test and validate the therapeutic potential of elevating the biology associated with reduced relapse in human tumours. RESULTS: By performing our analyses specifically within the stroma-rich/high-fibroblast (HiFi) subtype of CC, we identify and validate the clinical value of a HiFi-specific prognostic signature (HPS), which stratifies tumours based on STAT1-related signalling (High-HPS v Low-HPS=HR 0.093, CI 0.019 to 0.466). Using in silico, in vitro and in vivo models, we demonstrate that the HPS is associated with antigen processing and presentation within discrete immune lineages in stroma-rich CC, downstream of double-stranded RNA and viral response signalling. Treatment with the TLR3 agonist poly(I:C) elevated the HPS signalling and antigen processing phenotype across in vitro and in vivo models. In an in vivo model of stroma-rich CC, poly(I:C) treatment significantly increased systemic cytotoxic T cell activity (p<0.05) and reduced liver metastases (p<0.0002). CONCLUSION: This study reveals new biological insight that offers a novel therapeutic option to reduce relapse rates in patients with the worst prognosis CC.

Sensitivity of musculoskeletal model-based lumbar spinal loading estimates to type of kinematic input and passive stiffness properties.

The study investigated the potential for obtaining more accurate spine joint reaction force (JRF) estimates from musculoskeletal models by incorporating dynamic stereo X-ray imaging (DSX)-based in vivo lumbar vertebral rotational and translational kinematics compared to generic, rhythm (RHY)-based kinematics, while also observing the influence of accompanying inputs: intervertebral segment stiffness and neutral state. A full-body OpenSim® musculoskeletal model, constructed by combining existing lower- and upper-body models, was driven based on one volunteer's (female; age 25; 60.8 kg; 176 cm) anthropometrics and kinematics from a series of upright standing and straight-legged dynamic lifting tasks. The lumbar spine portion was modified in a step-wise manner to observe effects of: (1) RHY vs. DSX lumbar kinematics; (2) No disc (bushing) stiffness (NBS); generic, linear bushing stiffness (LBS); subject-specific nonlinear bushing stiffness (NLBS); (3) Upright standing (UP) vs. Supine (SUP) neutral state; (4) Weight lifted: 4.5 kg vs. 13.6 kg. L4L5 JRF from 24 model variations based on combinations of aforementioned parameters were compared. Rhythm-based kinematics without translational components tends to over-predict JRF (31% and 39% for compression and shear, respectively) compared to DSX-based kinematics. Additionally, differences due to accompanying passive stiffness and neutral state choice combinations were even larger (>50%), indicating heightened demand on the quality of these accompanying inputs. The study not only highlights model sensitivity to choices made regarding the three primary inputs-kinematics, passive stiffness and neutral state- separately, but also how interactions between these choices can result in significant variability in joint loading estimates.

A Dynamic Radiographic Imaging Study of Lumbar Intervertebral Disc Morphometry and Deformation In Vivo.

Intervertebral discs are important structural components of the spine but also are significant sources of morbidity, especially for the "low back" lumbar region. Mechanical damage to, or degeneration of, the lumbar discs can diminish their structural integrity and elicit debilitating low back pain. Advancement of reparative or regenerative means to treat damaged or degenerated discs is hindered by a lack of basic understanding of the disc load-deformation characteristics in vivo. The current study presents an in vivo analysis of the morphometry and deformation of lumbar (L2-S1) intervertebral discs in 10 healthy participants while performing a common lifting act, using novel dynamic radiographic imaging of the lumbar vertebral body motion. Data analyses show uniquely different (p < 0.05) characteristics in morphometry, normal and shear strain patterns of the L5S1 discs, while the rest of lumbar discs exhibit great similarity. In particular shear strains in L2-L5 discs exhibited stronger linear correlations (R2 ≥ 0.80) between strain changes and amount of lumbar flexion-extension motion compared to L5S1 (R2 ≤ 0.5). The study therefore advances the state of knowledge on in vivo mechanical responses of the lumbar intervertebral discs during functional tasks.

Lumbar Facet Joint Kinematics and Load Effects During Dynamic Lifting.

OBJECTIVE: To examine the lumbar facet joint kinematics in vivo during dynamic lifting and the effects of the load lifted. BACKGROUND: Although extensive efforts have been dedicated to investigating the risk factors of low back pain (LBP) associated with load handling in the workplace, the biomechanics of lumbar facet joints during such activities is not well understood. METHOD: Fourteen healthy participants performed a load-lifting task while a dynamic stereo-radiography system captured their lumbar motion continuously. Data from 11 participants were included for subsequent analysis. A randomized block design was employed to study the load effect (4.5 kg, 9.0 kg, and 13.5 kg) on bilateral facet joint motions at approximately 60°, 40°, 20°, and 0° trunk-flexion postures. The facet orientations were also examined. RESULTS: Significant load effects were found for the flexion and lateral bending and superior-inferior translation of the facet joints. The L5-S1 displayed greater lateral bending and twisting, which was due to its more posterolateral orientation than the L2-L3, L3-L4, and L4-L5 facet joints. The left-right asymmetry in facet orientation was observed, most prominently at L3-L4 and L5-S1 facet joints. CONCLUSION: The lumbar facet joint kinematics are affected by the magnitude of the lifted load and are dependent on the orientations of articulating adjacent facets. APPLICATION: This study provided new insights into the role of lumbar facet joints in vivo during lifting. Alterations in the facet joint kinematics due to vigorous functional demand can be one of the primary but overlooked mechanical factors in the causation of LBP.

Segmental variations in facet joint translations during in vivo lumbar extension.

The lumbar facet joint (FJ) is often associated with pathogenesis in the spine, but quantification of normal FJ motion remains limited to in vitro studies or static imaging of non-functional poses. The purpose of this study was to quantify lumbar FJ kinematics in healthy individuals during functional activity with dynamic stereo radiography (DSX) imaging. Ten asymptomatic participants lifted three known weights starting from a trunk-flexed (∼75°) position to an upright position while being imaged within the DSX system. High resolution computed tomography (CT) scan-derived 3D models of their lumbar vertebrae (L2-S1) were registered to the biplane 2D radiographs using a markerless model-based tracking technique providing instantaneous 3D vertebral kinematics throughout the lifting tasks. Effects of segment level and weight lifted were assessed using mixed-effect repeated measures ANOVA. Superior-inferior (SI) translation dominated FJ translation, with L5S1 showing significantly less translation magnitudes (Median (Md) = 3.5 mm, p < 0.0001) than L2L3, L3L4, and L4L5 segments (Md = 5.9 mm, 6.3 mm and 6.6 mm respectively). Linear regression-based slopes of continuous facet translations revealed strong linearity for SI translation (r2 > 0.94), reasonably high linearity for sideways sliding (Z-) (r2 > 0.8), but much less linearity for facet gap change (X-) (r2 ∼ 0.5). Caudal segments (L4-S1), particularly L5S1, displayed greater coupling compared to cranial (L2-L4) segments, revealing distinct differences overall in FJ translation trends at L5S1. No significant effect of weight lifted on FJ translations was detected. The study presents a hitherto unavailable and highly precise baseline dataset of facet translations measured during a functional, dynamic lifting task.