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Supplementary Figure from 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.

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

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.

Neck injuries are significant causes of morbidity and mortality, and their chronic forms due to repetitive or sustained physical acts (e.g., prolonged use of mobile phone with a dropped head) are becoming increasingly more prevalent. Many injuries are preventable but the prevention and control requires a clear basic understanding of the neck biomechanics. In this paper, we describe a first-of-its-kind study that integrates a gamut of state-of-the-art imaging modalities (dynamic radiography, computed tomography (CT), and magnetic resonance imaging (MRI)) and biodynamic measurements (motion capture, electromyography (EMG), force sensing), thereby investigating holistically the in vivo responses of the neck and its various interconnected musculoskeletal components during functional activities. We present a sample of findings to illustrate how the integrations at multiple levels can enable creating truly subject-specific neck musculoskeletal models and attaining novel insights that otherwise would be unattainable by a singular or subset of approaches.

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.

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 (r(2) > 0.94), reasonably high linearity for sideways sliding (Z-) (r(2) > 0.8), but much less linearity for facet gap change (X-) (r(2) ~ 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.

Although extensive efforts have been dedicated to investigating the risk factors of low back pain in the workplace, a clear knowledge of their effects on the facet joint (FJ) mechanics is lacking. In this study, fourteen healthy participants performed dynamic lifting task with varying external load while a dynamic stereo-radiography system captured their lumbar motion continuously. The FJ kinematics in the lumbar spine were ascertained using a volumetric model-based tracking method. The FJ kinematics data from seven participants were processed and analyzed using non-parametric statistical test. The results indicated significant (p

Neck musculoskeletal disorders have been associated with various occupational tasks, in particular tasks that require non-neutral sustained exertions. To gain a clear understanding of the neck biomechanics during such exertions, we have recently initiated an unprecedented integration of multi-modality state-of-the-art measurement procedures including dynamic radiographic imaging, surface-based motion capture, electromyography, computed tomography and magnetic resonance imaging. This paper describes an overview of our systematic, integrative efforts of in vivo biodynamic measurements during sustained-till- exhaustion neck exertions and multi-modality imaging data, and how such an integrated database can be used to construct subject-specific neck musculoskeletal models. A complete dataset of one participant is presented to illustrate the acquired data. In the next phase, subject-specific ‘what-if’ computer simulations will be implemented to understand the mechano-physiological effects of sustained-till-exhaustion neck exertions for different work scenarios and worker characteristics in order to derive effective injury prevention and intervention strategies.

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 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.

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.

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.

The present study aimed to establish a normative database of the neck muscular strength and the endurance capacity of a healthy population and to explore effects of neck posture, time-of-day (i.e., diurnal variation), and personal characteristics, such as age, sex, and body mass index on neck strength and endurance capacity during sustained-till-exhaustion maximal neck exertions. The multiple regression analyses revealed that sex and neck posture were significant predictors of neck strength, whereas time-of-day and age were significant predictors of neck endurance capacity.