Objective

To evaluate radiographic subchondral trabecular bone texture (TBT) as a predictor of clinically relevant osteoarthritis (OA) progression (combination of symptom and structural worsening).

Methods

The Foundation for the National Institutes of Health (FNIH) OA Biomarkers Consortium undertook a study of progressive knee OA cases (n = 194 knees with both radiographic and pain progression over 24-48 months) and comparators (n = 406 OA knees not meeting the case definition). TBT parameters were extracted from a medial subchondral tibial region of interest by fractal signature analysis of radiographs using validated semiautomated software. Baseline TBT and time-integrated values over 12 and 24 months were evaluated for association with case status and separately with radiographic and pain progression status, adjusted for age, sex, body mass index, race, baseline Kellgren/Lawrence grade, baseline joint space width, Western Ontario and McMaster Universities Osteoarthritis Index pain score, and pain medication use. C statistics were generated from receiver operating characteristic curves.

Results

Relative to comparators, cases were characterized by thinner vertical and thicker horizontal trabeculae. The summed composite of 3 TBT parameters at baseline and over 12 and 24 months best predicted case status (odds ratios 1.24-1.43). The C statistic for predicting case status using the TBT composite score (0.633-0.649) was improved modestly but statistically significantly over the use of covariates alone (0.608). One TBT parameter, reflecting thickened horizontal trabeculae in cases, at baseline and over 12 and 24 months, predicted risk of any progression (radiographic and/or pain progression).

Conclusion

Although associations are modest, TBT could be an attractive means of enriching OA trials for progressors since it can be generated from screening knee radiographs already standard in knee OA clinical trials.

Objective

To determine the optimal combination of imaging and biochemical biomarkers for use in the prediction of knee osteoarthritis (OA) progression.

Methods

The present study was a nested case-control trial from the Foundation of the National Institutes of Health OA Biomarkers Consortium that assessed study participants with a Kellgren/Lawrence grade of 1-3 who had complete biomarker data available (n = 539 to 550). Cases were participants' knees that had radiographic and pain progression between 24 and 48 months compared to baseline. Radiographic progression only was assessed in secondary analyses. Biomarkers (baseline and 24-month changes) that had a P value of <0.10 in univariate analysis were selected, including quantitative cartilage thickness and volume on magnetic resonance imaging (MRI), semiquantitative MRI markers, bone shape and area, quantitative meniscal volume, radiographic progression (trabecular bone texture [TBT]), and serum and/or urine biochemical markers. Multivariable logistic regression models were built using 3 different stepwise selection methods (complex models versus parsimonious models).

Results

Among baseline biomarkers, the number of locations affected by osteophytes (semiquantitative), quantitative central medial femoral and central lateral femoral cartilage thickness, patellar bone shape, and semiquantitative Hoffa-synovitis predicted OA progression in most models (C statistic 0.641-0.671). In most models, 24-month changes in semiquantitative MRI markers (effusion-synovitis, meniscal morphologic changes, and cartilage damage), quantitative central medial femoral cartilage thickness, quantitative medial tibial cartilage volume, quantitative lateral patellofemoral bone area, horizontal TBT (intercept term), and urine N-telopeptide of type I collagen predicted OA progression (C statistic 0.680-0.724). A different combination of imaging and biochemical biomarkers (baseline and 24-month change) predicted radiographic progression only, which had a higher C statistic of 0.716-0.832.

Conclusion

The present study highlights the combination of biomarkers with potential prognostic utility in OA disease-modifying trials. Properly qualified, these biomarkers could be used to enrich future trials with participants likely to experience progression of knee OA.

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale^1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter⁴; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation^5,6; analyses timings and patterns of tumour evolution⁷; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity^8,9; and evaluates a range of more-specialized features of cancer genomes^8,10-18.