There is evidence that multiple sclerosis (MS) pathology leads to distinct patterns of volume loss over time (VLOT) in different central nervous system (CNS) structures. We aimed to use such patterns to identify patient subgroups. MS patients of all classical disease phenotypes underwent annual clinical, blood, and MRI examinations over 6 years. Spinal, striatal, pallidal, thalamic, cortical, white matter, and T2‐weighted lesion volumes as well as serum neurofilament light chain (sNfL) were quantified. CNS VLOT patterns were identified using principal component analysis and patients were classified using hierarchical cluster analysis. 225 MS patients were classified into four distinct Groups A, B, C, and D including 14, 59, 141, and 11 patients, respectively). These groups did not differ in baseline demographics, disease duration, disease phenotype distribution, and lesion‐load expansion. Interestingly, Group A showed pronounced spinothalamic VLOT, Group B marked pallidal VLOT, Group C small between‐structure VLOT differences, and Group D myelocortical volume increase and pronounced white matter VLOT. Neurologic deficits were more severe and progressed faster in Group A that also had higher mean sNfL levels than all other groups. Group B experienced more frequent relapses than Group C. In conclusion, there are distinct patterns of VLOT across the CNS in MS patients, which do not overlap with clinical MS subtypes and are independent of disease duration and lesion‐load but are partially associated to sNfL levels, relapse rates, and clinical worsening. Our findings support the need for a more biologic classification of MS subtypes including volumetric and body‐fluid markers. In this work, we identified distinct patterns of volume loss over time in different central nervous system structures of multiple sclerosis patients. Using these patterns, we were able to robustly classify multiple sclerosis patients in separate groups. These groups did not overlap with standard clinical multiple sclerosis subtypes  and were independent of disease duration and lesion‐load, but were partially associated with serum neurofilament light chain levels, relapse rates and clinical worsening. For example, even early relapsing–remitting MS patients demonstrate clear signs of progressive clinical worsening independent of relapses  from a single center  were analyzed, retrospectively. All of the patients participating in our study have been previously reported  by trained and certified examiners, the dominant hand and non‐dominant hand 9‐hole peg test (D9HPT and ND9HPT) as well as the timed 25‐ft walk test (T25fwt). The occurrence of new relapses was recorded at every visit. Serum samples were collected on the same day as the clinical visit and serum neurofilament light chain (sNfL) levels were measured by Simoa assay as previously described , which also covered the upper‐cervical spinal cord. Additionally, a double echo SE proton density/T2‐weighted sequence was acquired .All MRI scans were performed on the same 1.5 T Magnetom Avanto MR scanner . The MRI‐protocol included a high‐resolution three‐dimensional T1‐weighted magnetization‐prepared rapid gradient‐echo (MPRAGE) sequence of the brain, acquired in sagittal orientation   >90% of total variance retained, (2) eigenvalue >0.7, (3) “broken stick rule,” (4) scree plot “elbow rule”  over 6 years was performed using separate linear mixed effect models (LMER) in order to explore between‐group differences. For both model types, this was done using a random intercept and a random time‐slope for each subject to allow for within‐subject and between‐subject variance. Independent variables were entered blockwise keeping the following sequence: first demographics, then disease duration and finally the aforementioned classification result. Each variable was tested both for its correlation to the intercept as well as to the slope over time of the dependent variable. All independent variables without statistical significance were excluded from the final model. Within‐group comparisons between CNS structures were performed using paired sample We performed a Cox regression analysis in a backwards stepwise fashion including sex, age, disease duration, and the classification outcome of our analysis to evaluate differences between the identified MS patient groups in terms of disease progression and time‐to‐disease progression. Clinical disease progression was defined according to the following conventions: (1a) an increase of 1 point in the EDSS if the baseline EDSS score was ≤5.5 or (1b) an increase of 0.5 point in the EDSS if the baseline EDSS score was >5.5, and (2) no relapse in the last 12 months.33.1After excluding MRI sessions with segmentation failures or errors  in either of the quantified CNS regions and patients with availability of only 1 MRI dataset  as well as patients diagnosed with clinically isolated syndrome that did not convert to clinically definite MS during follow‐up , a total of 226 patients, and 1,080 examination datasets  were entered in the statistical analysis.In a first step, we performed a regression analysis between the annual atrophy rates of the spinal cord, striatum, globus pallidus, thalamus, cortical gray matter, and brain white matter on one side and age on the other. Subsequently, we performed a principal component analysis using the residuals from each regression analysis. The first four principal components accounted cumulatively for 92% of the total data‐variance and were kept in further statistical analysis. Table The dendrogram of the hierarchical clustering is shown in Figure Cross‐validation of the clustering process with 10,000‐fold repetition showed a clustering agreement with a mean adjusted Rand index of 0.72 ± 0.17 (with 0 corresponding to random clustering results and 1 to absolute agreement between clustering outcomes) and a Meila's variation of information distance of 0.57 ± 0.30 —corresponding to absolute disagreement between clustering outcomes).3.2d of spinal cord and thalamus compared to all other structures: 0.56–1.11 and 0.40–1.46). Group B was characterized by a more prominent pallidal (−0.93%/year) VLOT compared to all other structures . Between‐structure differences of VLOT were generally rather small in Group C. Finally, Group D demonstrated an increase of spinal cord (1.29%/year) and cortical gray matter (2.82%/year) volume over time and a more prominent brain white matter VLOT (−1.37%/year) .Mean annual atrophy rates of the spinal cord, striatum, globus pallidus, thalamus, cortical gray matter, and brain white matter volumes as well as within‐group differences between the atrophy rates of all CNS‐structures and the respective size effects are reported in Table 3.3Detailed results for each analyzed CNS structure and T2LV are displayed in Table 3.4Trends of raw values of EDSS, T25fwt, and 9HPT in Groups A, B, C, and D over 6 years are displayed in Figure 3.4.1p < .01) and C , but a similar average logEDSS compared to Group D. The average logEDSS also did not differ between Groups B, C, and D. LogEDSS change over time did not differ between all groups.Between‐group differences of the average logEDSS were assessed using LMER analyses after correcting for sex, age, and disease duration. Group A had worse average logEDSS compared to Groups B . The average T25fwt−1 did not differ between Groups B, C, and D. Moreover, T25fwt−1 worsening over time was faster for Group A compared to all other groups . T25fwt−1 changes over time did not differ between Groups B, C, and D.Between‐group differences of the average T25fwt3.4.3p < .001) and C , but a similar average logD9HPT compared to Group D. The average logD9HPT did not differ between Groups B, C, and D. Moreover, the logD9HPT worsening over time was faster for Group A compared to Group B  and Group C , but similar compared to Group C. Changes of logD9HPT over time did not differ between Groups B, C, and D.Between‐group differences of the average logD9HPT were assessed using LMER analyses after correcting for sex and age. Group A had worse average logD9HPT compared to Groups B . The average logND9HPT did not differ between Groups B, C, and D. Moreover, the logND9HPT worsening over time was faster for Group A compared to all other groups . LogND9HPT worsening over time was similar between GROUPs B, C, and D.Between‐group differences of the average logND9HPT were assessed using LMER analyses after correcting for sex, age, and disease duration. Group A had worse average logND9HPT compared to all other groups , but did not differ between all other groups .The mean annual number of relapses was higher for Group B (0.45 ± 0.57 relapses/year) compared to Group C (0.24 ± 0.36 relapses/year) . Group B had a trend to higher mean sNfL compared to Group C (p = .066). All other between‐group differences were not statistically significant. Raw mean serum sNfL‐values between groups are shown in Figure After correcting for age, mean sNfL of all available time points was higher for Group A (51.4 ± 27.5 pg/mL) compared to all other groups  were represented in each of these groups. The 10,000‐fold cross‐validation analysis showed good agreement between the resulting clusters, supporting the robustness of our results in this MS‐population. In summary, our findings clearly add to the evidence currently challenging applied clinical definitions  was generally more severe and faster in Group A  compared to all other groups. This finding is in line with recent studies pointing to strong correlations of spinal cord and thalamic volume loss to clinical disease progression , so that this metric could not be included in our work. Symptomatic and asymptomatic spinal cord lesions currently have a central role in the correct diagnosis . In addition, because of the insufficient number of patients, we could not validate our results using a training, a validation and a test dataset; therefore, the existence of other potential groups, which could possibly be identified with our method in a larger population, cannot be excluded. For this reason, larger longitudinal multi‐center studies would be required to validate our results in the future. The lack of a control group to compare VLOT between the MS‐groups and healthy subjects is a clear limitation of our study, despite the fact that we accounted for the effects of normal aging in our statistical analyses. For this reason, we were not able to assess differences between patterns of CNS volume loss over time in “healthy” aging and multiple sclerosis. An effect of treatment on the results of our classification cannot be excluded. Indeed, patients of Group A were less commonly treated compared to Group C, whereas the distribution of treatments used in Group C was different compared to Group D. However, it is unclear, if and to which extent this could influence the presence of distinct patterns of VLOT in our patients and consequently our classification results. Nevertheless, the majority of our patients were treated with first‐line injectables, which have been shown to have a negligible effect on CNS‐atrophy  received speakers' honoraria from Novartis and ExceMED and travel support by Novartis Switzerland.Laura Gaetano is an employee of F. Hoffmann‐La Roche Ltd, Basel, Switzerland.Athina Papadopoulou has consulted for Teva, received speaker‐fee from Sanofi‐Genzyme and travel support from Bayer AG, Teva, UCB‐Pharma AG and Roche. Her research was/is being supported by the University of Basel, the Swiss Multiple Sclerosis Society, the Swiss National Science Foundation and the “Stiftung zur Förderung der gastroenterologischen und allgemeinen klinischen Forschung sowie der medizinischen Bildauswertung.”Jens Wuerfel: CEO of MIAC AG, Basel, Switzerland; speaker honoraria ; advisory boards and research grants ; supported by the German Ministry of Science (BMBF/KKNMS) and German Ministry of Economy (BMWi).Ludwig Kappos' institution  has received research support and payments that were used exclusively for research support for Dr Kappos' activities as principal investigator and member or chair of planning and steering committees or advisory boards in trials sponsored by Actelion, Addex, Almirall, Bayer HealthCare, Celgene, CLC Behring, Genentech, GeNeuro, Genzyme, Merck Serono, Mitsubishi Pharma, Novartis, Octapharma, Ono, Pfizer, Receptos, F. Hoffmann‐La Roche, Sanofi‐Aventis, Santhera, Siemens, Teva, UCB, and XenoPort; license fees for Neurostatus 4 products; research grants from the Swiss Multiple Sclerosis Society, the Swiss National Science Foundation, Innosuisse, the European Union, and the Roche Research Foundation.The current (DKD Helios Klinik Wiesbaden) or previous  institutions of Till Sprenger have received payments for speaking or consultation from: Biogen Idec, Eli Lilly, Allergan, Actelion, ATI, Mitsubishi Pharma, Novartis, Genzyme and Teva. Dr. Sprenger received research grants from the Swiss MS Society, Novartis Pharmaceuticals Switzerland, EFIC‐Grünenthal grant, and Swiss National Science Foundation.Dr. Kuhle reports grants from Biogen, grants from Novartis, grants from Roche, grants from Swiss MS Society, grants from Sanofi, grants from University of Basel, grants from Swiss National Research Foundation, grants from Merck, grants from Celgene, grants from Progressive MS Alliance, outside the submitted work.Stefano Magon is an employee of F. Hoffmann‐La Roche Ltd, Basel, Switzerland.The local ethics committee approved the study (EKBB‐46/04).All patients provided written informed consent.TABLE S1 Correlation matrix between the residuals of the mean annual atrophy rates of multiple sclerosis patients after correcting for ageClick here for additional data file.


Given the age-related decline in glomerular filtration rate (GFR) in healthy individuals, we examined the association of all-cause death or cardiovascular event with the Kidney age - Chronological age Difference (KCD) score, whereby an individual’s kidney age is estimated from their estimated GFR (eGFR) and the age-dependent eGFR decline reported for healthy living potential kidney donors.2 (eGFR60) in a community-based high cardiovascular risk cohort of 3837 individuals aged ≥60  years, followed for a median of 5.6 years.We examined the association between death or cardiovascular event and KCD score, age-dependent stepped eGFR criteria (eGFRstep), and eGFR < 60 ml/min/1.73 mIn proportional hazards analysis, KCD score ≥ 20 years (KCD20) was associated with increased risk of death or cardiovascular event in unadjusted analysis and after adjustment for age, sex and cardiovascular risk factors. Addition of KCD20, eGFRstep or eGFR60 to a cardiovascular risk factor model did not improve area under the curve for identification of individuals who experienced death or cardiovascular event in receiver operating characteristic curve analysis. However, addition of KCD20 or eGFR60, but not eGFRstep, to a cardiovascular risk factor model improved net reclassification and integrated discrimination. KCD20 identified individuals who experienced death or cardiovascular event with greater sensitivity than eGFRstep for all participants, and with greater sensitivity than eGFR60 for participants aged 60–69 years, with similar sensitivities for men and women.In this high cardiovascular risk cohort aged ≥60 years, the KCD score provided an age-adapted measure of kidney function that may assist patient education, and KCD20 provided an age-adapted criterion of eGFR-related increased risk of death or cardiovascular event. Further studies that include the full age spectrum are required to examine the optimal KCD score cut point that identifies increased risk of death or cardiovascular event, and kidney events, associated with impaired kidney function, and whether the optimal KCD score cut point is similar for men and women.NCT00400257, NCT00604006, and NCT01581827.ClinicalTrials.gov The online version contains supplementary material available at 10.1186/s12882-021-02324-y. The first limitation is illustrated by the association of eGFR 60–74 ml/min/1.73 m2 with increased risk of death and CV event in individuals aged < 65 years [Chronic kidney disease (CKD) “is defined as abnormalities of kidney structure or function, present for >3 months, with implications for health” , which i2 . Howeverpulation , and is pulation –18. Two 65 years , 20.2 for individuals aged < 40 years, < 60 ml/min/1.73 m2 for individuals aged 40–65 years, and < 45 ml/min/1.73 m2 for individuals aged > 65 years  score, whereby an individual’s kidney age is estimated from their eGFR and the age-dependent eGFR decline based on the metanalysis of 5482 healthy living potential kidney donors . In this2. Equation  in a prospective cohort study of 3837 community-based individuals at increased CV event risk who had eGFR measured at baseline. Our hypothesis was that the KCD score would be superior to eGFRstep in identifying individuals at increased risk of death or CV event.We compared the associations of all-cause death or CV event with the KCD score, the age-dependent stepped eGFR criteria of Delanaye et al.   study, a prospective cohort study of men and women recruited from the community, has been described elsewhere –27. A CODetails of the collection of baseline data are described elsewhere –27. SeruFollow-up was by participant visits and phone follow-up , systolic blood pressure, antihypertensive medication, and smoking status on enrolment). Serum lipids, measured on blood collected at Visit 2 . Integrated discrimination improvement (IDI) and calibration plots were calculated using sex-specific 5-year absolute risk derived from models based on CV risk factors alone, and from CV risk factor models to which KCD score ≥ 20 years (KCD20), eGFRstep or eGFR60 were added, using coefficients from multivariable proportional hazards models and Kaplan-Meier estimates of baseline event rate, as described by Goff et al. [P value < 0.05 was considered to indicate statistical significance. Analyses were conducted using Statview 5.0.1 , and R version 4.0.2.Continuous variables were summarised as medians  and categorical variables summarised as numbers (percentages). Study outcome was death or CV event . Data were censored at the date of last contact. Sensitivities and specificities for identification of individuals who experienced a study outcome were compared using McNemar’s test with Yates correction. Hazard ratios (HRs) and Bier scores, with 95% confidence interval (CI), for death or CV event were calculated from unadjusted proportional hazards models and also after adjustment for age, sex and CV risk factors , logf et al. . IDI wasf et al. . Given t2), 45% were overweight (25 < BMI < 30 kg/m2), 22% had a history of ischaemic heart disease, 10% had a previous myocardial infarction, and 10% had AF.Baseline participant characteristics are shown in Table Median follow-up was 5.6  years. Among participants who experienced death or CV event, median time to death or CV event was 3.4  years; 782 participants experienced death or CV event during follow-up, and 585 experienced death or CV event during 5 years of follow-up  had an eGFR < 60 ml/min/1.73 m0% had anBMI was similar for KCD score categories ranging from <− 20 to ≥30 years , and in separate analysis of participants aged 60–69 years (P < 0.0001), 70–79 years (P < 0.0001), and ≥ 80 years (P < 0.046)   or eGFRstep alone (P < 0.0001), with no statistically significant difference between the AUC for KCD20 alone and eGFRstep alone  was associated with increased risk of death or CV event. According to the full-age spectrum (FAS) equation , the lowcategory .It is of note that the 5482 healthy living potential kidney donors analysed by Pottel et al.  were sel2, and may assist in improving patient compliance with therapy.By providing an age-adapted measure of kidney function, the KCD score may assist in patient education. Informing a patient that their kidney function is equivalent to that of an individual 20 or more years older than the patient’s age may be more informative for the patient than telling them their eGFR result in ml/min/1.73 mOur finding that addition of KCD20, eGFRstep or eGFR60 to a CV risk factor model failed to improve the AUC beyond that of the model based on CV risk factors alone was in agreement with previous studies , and theOur study had a number of limitations. The SCREEN-HF cohort comprised volunteers  who were predominantly members of a health fund and, together with the inclusion criteria with respect to age ≥ 60 years and CV risk factors, may be cause for caution in the generalisation of our findings to the general community. However, the SCREEN-HF cohort was similar to the general Australian population aged ≥60 years. Of Australians aged 65–74 years, 53% have CV disease , 38.2% oA further limitation of our study was that our analysis was based on a single eGFR measurement for each participant. However, any misclassification would have biased our results toward the null hypothesis. Moreover, most of the CKD Prognosis Consortium analyses of eGFR and risk of adverse events in both high-risk and general populations used as the reference group participants with only a single eGFR available . Our equ2 did not have increased albuminuria [2 was associated with increased all-cause and cardiovascular mortality in the absence of increased albuminuria [We did not have urine data and therefore cannot report the proportion of participants with CKD according to urine albumin/creatinine ratio. Serum creatinine and eGFR are frequently measured as part of routine blood biochemistry, whereas the decision to measure albuminuria is usually based on an individual’s risk factor profile . Previouuminuria , 49, anduminuria . Howeveruminuria , and reduminuria . Thus, tIn summary, the KCD score provided an age-adapted measure of kidney function that may assist patient education, and a KCD score ≥ 20 years (KCD20) provided an age-adapted criterion of eGFR-related increased risk of death or CV event. KCD20 was more sensitive than eGFRstep in identifying individuals aged ≥60 years with increased risk of death or CV event, and more sensitive than eGFR60 in identifying individuals aged 60–69 years with increased risk of death or CV event. Further studies that include the full age spectrum are required to examine the optimal KCD score cut point that identifies increased risk of death or CV event, and kidney events, associated with impaired kidney function, and whether the optimal KCD score cut point is similar for men and women.Additional file 1.


Single‐arm trials are common in precision oncology. Owing to the lack of randomized counterfactual, resultant data are not amenable to comparative outcomes analyses. Difference‐in‐difference (DID) methods present an opportunity to generate causal estimates of time‐varying treatment outcomes. Using DID, our study estimates within‐cohort effects of genomics‐informed treatment versus standard care on clinical and cost outcomes.We focus on adults with advanced cancers enrolled in the single‐arm BC Cancer Personalized OncoGenomics program between 2012 and 2017. All individuals had a minimum of 1‐year follow up. Logistic regression explored baseline differences across patients who received a genomics‐informed treatment versus a standard care treatment after genomic sequencing. DID estimated the incremental effects of genomics‐informed treatment on time to treatment discontinuation (TTD), time to next treatment (TTNT), and costs. TTD and TTNT correlate with improved response and survival.Our study cohort included 346 patients, of whom 140 (40%) received genomics‐informed treatment after sequencing and 206 (60%) received standard care treatment. No significant differences in baseline characteristics were detected across treatment groups. DID estimated that the incremental effect of genomics‐informed versus standard care treatment was 102 days  on TTD, 91 days  on TTNT, and CAD$91,098  on costs. Effects were most pronounced in gastrointestinal cancer patients.Genomics‐informed treatment had a statistically significant effect on TTD compared to standard care treatment, but at increased treatment costs. Within‐cohort evidence generated through this single‐arm study informs the early‐stage comparative effectiveness of precision oncology. Difference‐in‐difference analysis is used to address confounding when analyzing real‐world data from a single‐arm precision oncology trial. Enrolled patients receiving genomics‐informed treatment are treated longer, suggesting improved efficacy, but at an increased cost compared to those receiving standard care. Despite potential for individual health benefit, comprehensive genomic sequencing is infrequently reimbursed by healthcare systems.Few randomized controlled trials (RCTs) have examined precision oncology’s efficacy.To identify a comparator for single‐arm trials that helps establish causality of effect and addresses selection bias, researchers are applying matching methods.In this study, we use DID for evaluating single‐arm precision oncology data.2Our retrospective cohort study uses data previously gathered in the BC Cancer POG program (NCT02155621), a single‐group research study in British Columbia, Canada.2.1Our study cohort was identified from adults who provided informed consent, enrolled in POG between July 2012 and August 2017, and received WGTA, as previously described.The study design and methodology are presented in Figure n = 40, 7%) and 2 , as corresponding data were note amenable to DID. Excluded patients may have been treated with radiation therapy or surgical resection rather than systemic therapy prior to or following sequencing, for which TTD and TTNT are not evaluable. Following WGTA results, excluded patients were either pending further treatment or experienced death or disease progression before WGTA data were acted on and, thus, did not have repeated outcomes data available for DID.We excluded patients who did not initiate a new line of systemic therapy in periods 1 , no clinically actionable findings generated through WGTA , no targeted therapies available or accessible to patients , and death or disease progression .From July 2012 to December 2017, 346 patients with advanced cancer were enrolled in the BC POG program and met our study criteria. Of these, 140 (40%) received a genomics‐informed treatment and 206 (60%) received standard care treatment after sequencing. A detailed list of all genomics‐informed treatments received during the period is provided in Table nstandard = 161, 78% and ngenomics = 106, 76%), and were diagnosed with breast cancers  or gastrointestinal cancers , most often colorectal. On average, participants were aged 55 (SDstandard: 12.24 and SDgenomics: 13.58), diagnosed with cancer in 2011 (SDstandard: 5.81) or 2010 (SDgenomics: 7.77), and received 2.6 (SDstandard: 1.66) or 2.7 lines (SDgenomics: 1.74) of systemic therapy prior to WGTA. Logistic regression results (Table p = 0.52).Table ts Table  indicaten = 21) of patients receiving genomics‐informed treatment paid out of pocket.POG enrollment was ongoing throughout the period and follow‐up times ranged from 1.12 years to 6.38 years. Median follow‐up was 2.83 years in genomics‐informed patients and 2.59 years in standard care patients. Incomplete follow up resulted in 6% of patients having censored TTD and treatment costs and 13% having censored TTNT in period 2. While the majority of treatment costs were reimbursed by BC’s public single‐payer system, 15% . TTNT was shorter and treatment costs were lower but not significant . Pre‐sequencing clinical outcomes were, thus, poorer for patients who received WTGA‐informed treatment compared to those continuing with standard care, suggesting baseline differences in responsiveness to standard care therapies or phase of treatment.Trends in time‐varying outcomes are depicted in Figure TTD,standard = −132 days, 95% CI: −179, −93, Δx̅TTD,genomics = −30 days, 95% CI: −78, 26, Δx̅TTNT,standard = −224 days, 95% CI: −296, −168, and Δx̅TTNT, genomics = −133 days, 95% CI: −216, −79), whereas trends in treatment costs were opposite across groups . Trends are indicative of increasingly shorter response times on subsequent lines of therapy as treatment resistance develops.Downward time trends in average TTD and TTNT were observed irrespective of treatment group  on TTD at a significantly increased treatment cost of $91,098 per patient . Sensitivity analysis reported in Table 3.2Stratifying our analysis according to the two most common cancer types revealed outcomes heterogeneity that was masked in the pooled results. Table 3.3cost,pre $−16,981. 95% CI:−$29,412, −$7,753) compared to usual care. We also found downward trends in TTNT over time among genomics‐informed and usual care patients  and downward trends in TTD and treatment costs among usual care patients .Similar to pooled analysis, we found that period 1 treatment costs for breast cancer were significantly lower among genomics‐informed patients  and downward trends in TTD, TTNT, and treatment costs were detected among usual care patients . For gastrointestinal cancers, patients who received WGTA‐informed treatment had significantly higher TTD, TTNT, and treatment cost compared to standard care. Incremental effects ranged from 284 days  on TTD to 327 days  on TTNT to $53,861  on treatment costs.Stratified results for patients with gastrointestinal cancers were largely consistent with pooled results. Significantly lower baseline outcomes were present in genomics‐informed versus usual care patients (Δx̅3.5p > 0.006). In period 1, genomics‐informed patients had weakly significantly shorter TTD . Significant downward trends in TTD and TTNT were observed among usual care patients . Patients with other cancers who received WGTA‐informed treatment experienced longer TTD (p = 0.054) and higher treatment costs (p = 0.021), although these were no longer statistically significant after Bonferroni correction. Incremental effects included 74 days  on TTD and $88,476  on treatment costs.Among patients diagnosed with other cancers, estimated magnitudes and directions often aligned with pooled results, although few differences remained significant after correcting for multiple comparisons , aligning with improved progression‐free survival ratios observed in prior within‐patient evaluations.p = 0.002 and p < 0.0001) and longer albeit not significant TTD in other cancers (p = 0.054), but had no effect within breast cancers. The benefits of tumor‐agnostic precision oncology will, thus, be driven by heterogeneous responses across cancer subtypes. Our results confirm that uniform coverage of genomic sequencing across all indications is unlikely to yield equal effectiveness or cost‐effectiveness for all patients.Estimated effects varied across tumor types. Genomics‐informed treatment resulted in significantly longer TTD and TTNT within gastrointestinal cancers . Genomics‐informed treatments cost more than standard care treatments, primarily owing to longer durations of use rather than high prices of on‐patent treatments. This effect was most prominent in gastrointestinal cancers. Higher treatment costs combined with upfront sequencing costs will have implications on cancer care budgets and the ability to pay for genomics‐guided interventions, whether by public healthcare systems, private payers, or patients.n = 48) did not substantively change effect estimates (Table Prior to sequencing, standard care patients had significantly higher TTD and treatment costs compared to patients receiving WGTA‐informed treatment, despite having similar enrollment characteristics. While unadjusted analyses across these patient groups would result in biased effect estimates, baseline outcomes differences do not directly threaten the validity of DID estimates.es Table . FurtherTo our knowledge, ours is the first study to estimate the comparative effectiveness of genomics‐informed versus standard care treatment and the first to consider DID for evaluating single‐arm precision oncology initiatives. The DID approach is common in health research and is well suited to within‐cohort estimation of causal time‐varying treatment effects.Owing to our study’s small sample size, subgroup analysis was only possible in select cancer subtypes. Future research powered to detect differences across all cancers is necessary to guide decision‐making. Treatment costs were also estimated using list prices where internal estimates were unavailable, which may overestimate actual expenditures. DID requires repeated outcomes data and POG patients who did not initiate a new treatment after WGTA, either owing to ongoing treatment response or death or disease progression, were not eligible for this study, affecting the cohort composition and generalizability of our study findings. Causality of our results also relies on the parallel trends assumption underlying DID analysis, which cannot be tested for in a two‐period setting. While early‐stage analyses of short‐term outcomes are least likely to be severely impacted by deviations from this assumption, there is an unmet need for quasi‐experimental methods development robust to time‐varying confounding.5In the continued absence of RCTs, DID is a tool to address confounding bias for precision oncology. By expanding the application of DID methods to evaluate single‐arm trial data, we find that genomics‐informed treatment has a statistically significant effect on TTD and treatment costs compared to standard care treatment in patients with advanced cancers. Our study will guide future quasi‐experimental within‐cohort evaluations enabling early‐stage estimation of precision oncology’s clinical and economic impacts. Broadening the spectrum of evidence generated throughout the technology life cycle will provide critical support for reimbursement decision‐making.Brandon Chan, Steven J.M. Jones, and Marco A. Marra report no conflicts of interest. Deirdre Weymann and Samantha Pollard codirect IMPRINT Research Consulting and have consulted for Roche Canada. Janessa Laskin has received honoraria for academic talks from: Roche Canada, Pfizer Canada, Astra‐Zeneca Canada, and BI Canada; her institution has received research funding for her projects from: Roche Canada, Asta‐Zeneca Canada, and BI Canada. Daniel J. Renouf disclosures include research funding and honoraria from Bayer and Roche, and travel funding and honoraria from Servier, Celgene, Taiho, Ipsen, and Astra Zenec. Howard Lim has received honoraria from Eisai, Taiho, Roche, Lilly, BMS, Amgen, and Leo for consultant work and is an investigator on trials with Bayer, BMS, Lilly, Roche, Astra‐Zeneca, and Amgen. Sophie Sun has received research grant and honoraria funding from Astra‐Zeneca. Stephen Yip is an advisory board member for and has received travel allowance from Amgen, AstraZeneca, Bayer, Norvatis, and Roche. Dean A. Regier has received speaking honoraria from Roche Canada. DF Schaeffer has received honoraria from Alimentiv, Pfizer, Merck, and Diaceutics. Kasmintan A. Schrader has received speaking honoraria from AstraZeneca and Pfizer.Study conception and design: All authors. Statistical analysis: DW, BC, and DAR. Drafting of the manuscript and final approval: All authors. Guarantor of work: DW and DAR.Table S1‐S6Click here for additional data file.


Leukoaraiosis (LA) refers to white matter lesions of undetermined etiology associated with the appearance and worsening of vascular pathologies. The aim is to confirm an increased frequency and intensity of LA in symptomatic patients with neurovascular pathology compared with asymptomatic subjects, and its association with circulating serum levels of soluble tumor necrosis factor‐like weak inducer of apoptosis (sTWEAK).N = 242) comprised of asymptomatic subjects with arterial hypertension and/or diabetes or with a history of transient ischemic attacks, and Group II (N = 382) comprised patients with lacunar stroke or deep hemispheric intracerebral hemorrhage (ICH) of hypertensive origin. Serum levels of sTWEAK were analyzed and correlated with prevalence and intensity of LA according to the Fazekas scale.An observational study was conducted in which two groups of patients were compared. Group I (p = 0.003). When stratified according to the Fazekas scale, LA of grade II  and specially grade III  showed correlation with neurovascular pathologies. Increased sTWEAK levels were found in the symptomatic group in all LA grades (p < 0.0001), and associated with 5.06 times more risk of presenting clinical symptoms .The prevalence of LA was higher in symptomatic (85.1%) versus asymptomatic patients (62.0%). Logistic regression model showed a significant relation of LA with neurovascular pathologies (OR: 2.69, IC 95%: 1.10–6.59, LA showed a higher prevalence in patients with symptomatic lacunar stroke or deep hemispheric ICH. There is an association between sTWEAK levels and LA degree. Leukoaraiosis (LA) is a radiological term referring to lesions in the white substance through imaging techniques, such as low density on computed tomography (CT), hyperintensity on T2‐weighted or FLAIR images in magnetic resonance image (MRI).A registry‐based observational study was conducted with a total of 624 patients consecutively and prospectively recruited from our databank, who were admitted to the Stroke Unit of the University Clinical Hospital of Santiago de Compostela (Spain). The recruitment period was from January 2008 through December 2018. Patients with hypertension or diabetes were included consecutively and evaluated on a prospective registry.1n = 242): Asymptomatic subjects ≥50 years with history of high blood pressure and/or diabetes of >5 years, without neurological vascular manifestations or cognitive deficit screened by primary care physicians (n = 105) and subjects with a history of transient ischemic attacks of the carotid territory, without cardioembolic features (n = 137) according to the TOAST classification.Group I (2n = 382): Symptomatic patients with hemispheric lacunar infarctions (n = 195) or deep intracerebral hemorrhages of hypertensive mechanism (n = 187).Group II . Informed consent was obtained from each patient or from their relatives after full explanation of the procedures.A magnetic resonance study was conducted for all the patients enrolled. Imaging of patients with vascular risk (Group I) was conducted in the first quarter after their enrollment in the study. For symptomatic patients (Group II), neuroimages were acquired at admission to the emergency department by neurologists of the stroke unit blinded to clinical and analytical data. The presence and severity of LA were assessed using the Fazekas scale with a total score from 0 to 6 .Circulating biomarkers were selected based on existing evidence from experimental and clinical studies.Measurements were performed in an independent laboratory blinded to clinical data.p‐values <0.05.Initially, a descriptive analysis of the sample was performed. Categorical variables were described as frequency and percentage, and the continuous variables as mean/standard deviation or median/interquartile range, according to their adjustment to a normal distribution (determined by the Kolmogorov–Smirnov test with the Lilliefors corrections). The statistical inference was carried out with the chi‐squared test, ANOVA, or Mann–Whitney test according to the nature of the contrast variable and its adjustment to normality. Data comparison was performed by means of Pearson test for categorical variables and Spearman's rho test for the continuous ones. Finally, a multivariable logistic regression was carried out adjusting for the significant variables found in the previous analysis. ORs and their 95% confidence intervals were calculated. All analyses were performed with IBM SPSS Statistics v.20. Statistical significance was set at 0  with a level of significance of 5%, assuming an expected LA prevalence of 70% in the asymptomatic group and 85% in the symptomatic group. A total of at least 203 subjects/patients should be enrolled in each cohort, which implies a minimum total sample of 406 patients distributed between the two balanced groups. The calculation was performed with Ene 3.0 program.The number of patients was estimated to achieve a power of 80% to detect differences in the null hypothesis HAll data supporting the findings of this study are available to any qualified investigator from the corresponding author upon request.N = 242) and symptomatic Group II (n = 382) were enrolled in this study. An initial descriptive analysis of the cohorts was performed to evaluate differences between groups (Table S1). Demographic variables showed statistical differences in the age of patients  and in the previous Rankin scale . Symptomatic patients exhibited a higher percentage of ischemic heart disease  and previous symptomatic stroke (0% vs 7.1% p = 0.019). When analyzing molecular variables, clinical inflammation markers such as leukocytes (7.3 ± 1.9 vs. 8.8 ± 2.9 × 103/mmc) and C‐reactive protein (1.29 ± 1.87 vs 4.51 ± 5.17 mg/L) were higher in the symptomatic group (p < 0.0001 for both variables). A similar association was found in microalbuminuria , probably due the pathological vascular condition of the patients.A total of 624 subjects, divided into asymptomatic Group I (p < 0.0001). We found statistical differences between groups when the sample was stratified according to the degree of LA (p < 0.0001) (Table p = 0.003). As this study was mainly focused on LA, the regression model was repeated including LA stratification from grade I to III. As highlighted in Table p = 0.003) and especially grade III  correlated with suffering a symptomatic lacunar stroke or deep hemispheric ICH. In this regard, it is particularly important to remark that LA grade I lost this correlation in the adjusted model .The descriptive bivariate study of neuroimaging variables showed an increased number of patients with LA in the symptomatic group compared to the asymptomatic group  Table . An adjup < 0.0001).sTWEAK serum levels were analyzed in the entire sample. As detailed in Figure p < 0.0001) higher levels of sTWEAK (7078.8 [4055.8–9344.5] pg/ml) compared to patients without LA findings (1644.8 [966.1–3801.4] pg/ml). In order to relate sTWEAK serum levels with LA severity, subjects were stratified according to LA grade as measured by the Fazekas scale. As outlined in Figure p < 0.0001) of sTWEAK, directly related to the increase in LA degree, evidencing the relationship between this molecule and LA severity.In addition, we analyzed the relationship of sTWEAK serum levels with the presence of LA in the whole sample as shown in Figure p < 0.0001) in sTWEAK levels in the symptomatic group compared to the asymptomatic group in all LA grades  and symptomatic lacunar stroke or deep hemispheric ICH (Group II). The analysis showed an increase .In line with these results, sTWEAK levels were included in a regression model using a neurovascular disease as dependent variable. As described in Table p = 0.073) while sTWEAK ng/ml increased its relationship with neurovascular symptomatology  as detailed in Table In order to evaluate the relationship between LA and sTWEAK, both variables were included in the same multivariable regression model. In this case, LA lost its significance in the adjusted model (OR: 1.58, 95% CI: 0.93–7.35, The pathogenesis of LA is still unknown and not fully understood, yet it is a common denominator in various neurological disorders. LA has been extensively associated with vascular irregularities and more specifically with cSVD.Currently, characterization by imaging techniques is limited and, in many cases, does not go beyond the use of the qualitative known scales.In this work, we conducted an observational study of a total of 624 patients divided into an asymptomatic and a symptomatic group. Compared with the asymptomatic subjects, symptomatic patients showed higher levels of inflammation markers such as C‐reactive protein and number of circulating leucocytes, as well as microalbuminuria, probably due to the presence of vascular pathologies,Although the involvement of the vascular component in the onset of the disease has gained strength, LA can currently be considered as a process of multiple origin, where protein expression patterns of the endothelium show a broad variabilityLA showed a higher prevalence in patients with symptomatic lacunar stroke or deep hemispheric ICH. There is an association between sTWEAK levels and LA degree, so further studies are necessary to explore sTWEAK as therapeutic target to mitigate LA progression associated with cerebrovascular diseases.AS‐C, JC, PH, and RI designed the study; AS‐C, AC, IL‐D, MR‐Y, and TS acquired clinical data for further analysis; PA and MA‐A elaborated tables and designed images; AS‐C, TS, FC, and JC analyzed the collected data and performed statistical analysis; AS‐C, JMP, and PH drafted the manuscript.The authors report no conflict of interest.Table S1. Bivariate analysis of demographic, molecular, and neuroimaging variables for asymptomatic and symptomatic groups.Figure S1. Serum levels of sTWEAK on admission in asymptomatic and symptomatic patients.Click here for additional data file.


DNA‐binding proteins, including transcription factors (TFs), play essential roles in various cellular processes and pathogenesis of diseases, deeming to be potential therapeutic targets. However, these proteins are generally considered undruggable as they lack an enzymatic catalytic site or a ligand‐binding pocket. Proteolysis‐targeting chimera (PROTAC) technology has been developed by engineering a bifunctional molecule chimera to bring a protein of interest (POI) to the proximity of an E3 ubiquitin ligase, thus inducing the ubiquitination of POI and further degradation through the proteasome pathway. Here, the development of oligonucleotide‐based PROTAC (O'PROTACs), a class of noncanonical PROTACs in which a TF‐recognizing double‐stranded oligonucleotide is incorporated as a binding moiety of POI is reported. It is demonstrated that O'PROTACs of lymphoid enhancer‐binding factor 1 (LEF1) and ETS‐related gene (ERG), two highly cancer‐related transcription factors, successfully promote degradation of these proteins, impede their transcriptional activity, and inhibit cancer cell growth in vitro and in vivo. The programmable nature of O'PROTACs indicates that this approach is also applicable to destruct other TFs. O'PROTACs not only can serve as a research tool but also can be harnessed as a therapeutic arsenal to target DNA binding proteins for effective treatment of diseases such as cancer. DNA binding proteins including transcription factors remain as challenging targets in drug discovery. oligonucleotide‐based proteolysis‐targeting chimeras (O'PROTACs), which employ unique DNA sequences as natural “ligand” of targeting proteins, are developed to effectively destruct lymphoid enhancer‐binding factor 1 (LEF1) and ETS‐related gene (ERG) and inhibit prostate cancer cell growth. O'PROTAC represents an attractive approach for treatment of diseases including cancer. Among them, ≈300 TFs are associated with cancer development, which accounts for ≈19% of oncogenes.1 Ther However, most other TFs are challenging to target because they lack a ligand‐binding pocket. As the knowledge regarding the mechanisms of the assembly of transcription complexes has increased exponentially, different strategies to modulate the activity of TFs with small‐molecule compounds have emerged, including blocking protein/protein interactions, protein/DNA interactions, or chromatin remodeling/epigenetic reader proteins. However, the development of traditional small molecules inhibiting non‐ligandable TFs still remains very challenging, and a new targeting strategy to overcome the hurdle is highly demanded.In the last decades, small molecule modulators have been developed to target nuclear receptors on the basis that this class of TFs contains a clearly defined ligand‐binding pocket.2 Howe PROTAC technology has significantly advanced during the last decade. It has been reported that PROTACs are capable of degrading more than 100 targets, and a few PROTACs have entered clinical trials. PROTACs offer several advantages over small molecule inhibitors, including expanding target scope, improving selectivity, reducing toxicity, and evading inhibitor resistance. This suggests that PROTAC technology is a new promising modality to tackle diseases, especially cancer. Most recently, PROTACs have been designed to degrade TFs. In 2019, Wang's group developed potent and signal transducers and activators of transcription 3 (STAT3)‐specific degrader based on the STAT3 inhibitor SI‐109 and demonstrated its targeting efficacy in vivo. Recently, Crews’ group reported the development of transcription factor targeting chimeras (TRAFTACs), which utilize haloPROTAC, dCas9‐HT7, and dsDNA/CRISPR‐RNA chimeras to degrade TFs. Nevertheless, this approach uses the artificially engineered dCas9‐HT7 fusion protein as a mediator, limiting its potential use in the clinic.Proteolysis‐targeting chimeras (PROTACs) are heterobifunctional small molecules composed of a protein of interest (POI) ligand as a warhead, a linker, and an E3 ligase ligand. The PROTAC molecule recruits the E3 ligase to the POI and induces the ubiquitination of the latter and further degradation by the proteasome pathway.4 PROTβ‐catenin, LEF1 promotes the transcription of Wnt target genes. LEF1 also can facilitate epithelial‐mesenchymal transition (EMT). Aberrant expression of LEF1 is implicated in several cancer types and related to cancer cell proliferation, migration, and invasion. Therefore, therapeutic targeting LEF1 is urgently necessary to treat prostate cancer patients effectively.Lymphoid enhancer‐binding factor 1 (LEF1) is a highly cancer‐related TF. It belongs to the T‐cell factor (TCF)/LEF1 family. Complexed with et genes.9 LEF1 It is significantly overexpressed in nearly 50% of all human prostate cancer cases, including primary and metastatic prostate cancer, due to the fusion of the ERG gene with the androgen‐responsive TMPRSS2 gene promoter.TMPRSS2‐ERG gene fusion results in aberrant overexpression of truncated ERG, implying that increased expression of ERG is a pivotal factor to drive prostate cancer progression. Hence, ERG is another attractive target for prostate cancer treatment.ETS‐related gene (ERG) transcription factor belongs to the erythroblast transformation‐specific (ETS) family and is involved in bone development, hematopoiesis, angiogenesis, vasculogenesis, inflammation, migration, and invasion.12 It Figure In the present study, we introduce a new strategy to target TFs using O'PROTACs, in which a double‐stranded oligonucleotide is incorporated as POI binding moiety in PROTAC Figure 1. We de22.1 LEF1 acts as a DNA binding subunit in the β‐catenin/LEF1 complex and exerts transcriptional regulation via binding to the nucleotide sequence 5′‐AAAGATCAAAGGGTT‐3′. We designed 18‐mer double‐stranded oligonucleotide (TACAAAGATCAAAGGGTT)‐based LEF1 O'PROTACs by including the LEF1 binding moiety (underscored) and three extra nucleotides for protection of oligo degradation  and mass spectrometry analysis, we found that VH 032‐based O'PROTACs (V1 to V3) were successfully constructed with high purity (>95%); however, the purity of pomalidomide‐based O'PROTACs (C1 to C3) was much lower. The nucleic acid‐related agents can be delivered into cells via lipid‐based approaches or other means. Fluorescein isothiocyanate (FITC) was incorporated in LEF1 O'PROTACs to determine the transfection efficiency. These FITC‐labeled LEF1 O'PROTACs were transfected into PC‐3 prostate cancer cells, which express endogenous LEF1 Figure . In contβ‐catenin/LEF1 complex. We found that LEF1 OP‐V1 treatment downregulated mRNA expression of CCND1 and c‐MYC, two known target genes of β‐catenin/LEF1 in both PC‐3 and DU145 cells  value of 25 nm ‐based ERG O'PROTACs by including ERG binding moiety (underscored) and three extra nucleotides for protection of oligo degradation , especially ERG OP‐C‐N1 potently degraded ERG protein in VCaP cells Figure . As withm Figure . Additiom Figure , suggestm Figure  and pomam Figure . In agrem Figure . To idenm Figure . Moreove3 in the current communication we systematically present the procedures of design and synthesis of LEF1 and ERG O'PROTACs, the mechanisms of action, and their anti‐cancer efficacy in vitro and in vivo. O'PROTAC is designed based on natural “ligand” of transcription factors, namely specific DNA sequence, attached to an E3 ligase ligand via a linker. Each TF recognizes fairly unique DNA sequences. Although transcription factors typically bind to the core sequence, usually ≈10 nt in length, the flanking bases confers the additional substrate specificity. Therefore, from those LEF1 and ERG respective binding sequences, the longest sequences were chosen as their binding motifs and expected to have better selectivity. Besides, three additional bases are added to 3′ end of the reverse strand to prevent the exonucleases‐mediated hydrolysis. Hence, double‐stranded oligonucleotides with length of 18 and 19 bp was used to develop LEF1 and ERG O'PROTACs, respectively. Our data also stress that potency of O'PROTACs varies and likely depends on the lengths and types of a linker as well as the E3 ligase ligand. While the exact underlying mechanisms are unclear at present, a comprehensive picture could emerge with more O'PROTACs designed and explored.Building upon our reporting of the previously uncharacterized strategy of O'PROTACs to destruct generally “undruggable” transcription factors in bioRxiv,19 in  Our data demonstrated that LEF1 OP‐V1 and ERG OP‐C‐N1 induced the initiation of degradation of LEF1 and ERG proteins at 12 and 24 h, respectively in prostate cancer cell lines examined. Due to the oligo‐based nature of O'PROTACs and because their delivery into cells likely takes longer time than small molecule PROTACs, our data suggest that O'PROTACs may act similarly on protein destruction as the conventional counterparts. Furthermore, hook effect has been observed with many PROTACs. The dose‐course experiments indicated that no obvious hook effect was detected for LEF1 OP‐V1. However, ERG OP‐C‐N1 might have certain hook effect, at least at the highest concentration we used (10 000 nm). Given that delivery of O'PROTACs relies on liposomes and polymers, they are limited to be used at very high concentrations such as in the order of micro molars. Therefore, it is unlikely that O'PROTACs could have any massive hook effect, at least not at the concentrations we can deliver into cells.Previous studies indicate that PROTACs can initiate protein degradation very fast, although the time points vary broadly from 1 to 16 h after degrader exposure.23 Our In comparison with classic small molecule drugs, PROTACs are significantly less druggable. Whereas O'PROTACs hold enormous potentials to transcend the limitations of conventional PROTACs; because of their modalities, degraders can be rationally programmed according to the DNA binding sequence of a given TF, thus theoretically making it possible to target any TF of interest. Moreover, the synthesis of O'PROTAC is straightforward and efficient, which facilitates the rapid development of an O'PROTAC library for high‐throughput screening of the most potent TF degraders.Conventional PROTAC technology is rapidly evolving, and some of PROTACs are in clinical trials; however, it inherits certain limitations. First, most of the reported PROTACs rely on the existing small molecules as POI targeting warhead, making it difficult to be applied to “undruggable” targets like TFs. Additionally, due to their high molecular weight (up to 1400 Da), PROTACs suffer from poor cell permeability, stability, and solubility.25 In  which utilize single‐stranded RNA (ssRNA) to recruit RNA‐binding protein (RBP). The binding of RBP with RNA heavily relies on both sequence motif and secondary structure. Predicting the interaction between RNA and RBP is challenging due to the high flexibility of RNA. However, double‐stranded DNA bears a well‐defined 3D duplex structure; therefore, the protein binding region is accessible and more predictable. Hence, O'PROTAC is programmable by changing the nucleotide sequence that binds protein. Additionally, compared with double‐stranded oligonucleotide, ssRNA is susceptible to deleterious chemical or enzymatic attacks. On the contrary, O'PROTAC is desirable for drug development due to its ready predictability and superior stability.Hall and colleagues recently report RNA‐PROTACs,26 whi The catalytic advantage of PROTACs incorporated into oligonucleotide drugs could further advance the field. Moreover, the delivery of oligonucleotide drugs has been improved significantly in recent years, such as nucleic acid chemical modifications, conjugation to cell/tissue‐targeting ligands, and nanoparticle carrier systems. Therefore, O'PROTAC can be a complementary drug discovery and development platform to conventional PROTACs to derive clinical candidates and accelerate drug discovery.Oligonucleotide‐based drug development has become mainstream for new drug hunting in the last decade, and several oligonucleotides have been FDA‐approved.29 TheIn summary, we present a proof‐of‐concept of O'PROTAC by identifying the potent and highly efficacious LEF1 and ERG O'PROTACs in vitro and in vivo. Our findings, especially the in vivo efficacy data lays a solid foundation for the further development of O'PROTAC as a new class of therapy for the treatment of human cancers or other diseases in which transcription factors or other DNA binding proteins play a crucial role.The authors declare no conflict of interest.J.S., Y.Y., and D.D. contributed equally to this work. H.H. and H.‐Y.L. contributed to the conception and design; J.S., W.Y., and A.K. contributed to the O'PROTAC synthesis; Y.Y., D.D., D.W., Y.H., and Y.P. contributed to the acquisition of data; H.H., H.‐Y.L., J.S., Y.Y., D.D., W.Y., and A.K. contributed to the preparation of the manuscript.Supporting InformationClick here for additional data file.


The new 2018 pulmonary hypertension (PH) definition includes a lower mean pulmonary artery pressure (mPAP) cut‐off (>20 mmHg rather than ≥25 mmHg) and the compulsory requirement of a pulmonary vascular resistance (PVR) ≥3 Wood units (WU) to define precapillary PH. We assessed the clinical impact of the 2018 compared to the 2015 PH definition in aortic stenosis (AS) patients undergoing aortic valve replacement (AVR).Severe AS patients (n = 487) undergoing pre‐AVR right heart catheterization were classified according to the 2015 and 2018 definitions. Post‐AVR mortality (median follow‐up 44 months) was assessed.Based on the 2015 definition, 66 (13%) patients exhibited combined pre and postcapillary PH (CpcPH), 116 (24%) isolated post‐capillary PH (IpcPH), 28 (6%) precapillary PH, and 277 (57%) no PH at all. Overall, 52 (11%) patients were reclassified: 23 no PH into IpcPH; 8 no PH into precapillary PH; 20 precapillary PH into no PH; 1 CpcPH into IpcPH. By the 2015 definition, only CpcPH patients displayed increased mortality, whereas by the 2018 definition, precapillary PH patients also experienced higher mortality than those without PH. Among the PH definition components, PVR ≥3 WU was the strongest predictor of death (hazard ratio > 4).In severe AS, a higher number of IpcPH patients are diagnosed by the 2018 definition, even though they have the same prognosis as those without PH. Patients with true precapillary PH are more accurately identified by the 2018 definition that includes a pulmonary vascular disease criterion, that is, PVR ≥3 WU, a strong mortality predictor. Patients underwent coronary angiography using 5‐ or 6‐French catheters via femoral or radial artery access, and right heart catheterization using 6‐French Swan‐Ganz catheters via femoral or brachial access. Routine right heart catheterization at the time of coronary angiography has been our practice for more than 20 years in patients with AS evaluated for AVR. The midthoracic level was used as zero reference point. Systolic pulmonary artery pressure (sPAP), diastolic pulmonary artery pressure (dPAP), mPAP, and pulmonary artery wedge pressure were measured. Measurements were obtained at end‐expiration, while avoiding patient's breath‐holding and especially Valsalva maneuvers, with the mean of three to five measurements taken. The mPAWP was calculated over the entire cardiac cycle, and V waves were included. This practice leads to higher values compared to the measurement of the end‐diastolic pulmonary artery wedge pressure. However, for the estimation of the impact of the left heart contribution to pulmonary pressures and calculation of PVR respectively, mPAWP is preferred.2.3According on the 2015 ESC/ERS guidelines,2.4All patients underwent surgical (71%) or transcatheter (29%) AVR following a median interval of 21 (12–35) days post‐catheterization. Information on long‐term follow‐up was obtained from patients, general practitioners, and hospital or practice cardiologists. The endpoint was all‐cause mortality.2.5p‐value <.05 was considered statistically significant. Analyses were performed using SPSS statistical package Version 20.0 .Categorical data were presented as numbers and percentages, and continuous data as mean ± standard deviation or median (interquartile range), as appropriate. Patients from different hemodynamic PH categories and those without PH according to the 2015 and 2018 definitions were compared using chi‐square tests, analysis of variance, or Kruskal‐Wallis tests, as appropriate. Survival of patients from different PH groups and different hemodynamic categories were compared using Kaplan–Meier plots and log‐rank tests. Cox regression was applied to describe the association between variables of interest and mortality. A 33.12/m2, and the left ventricular ejection fraction 58 ± 12%. The mean mPAP, mPAWP, and PVR were 26 ± 10 mmHg, 16 ± 8 mmHg, and 2.2 ± 1.3 WU, respectively, and the median (interquartile range) DPG was 0 (−3–2) mmHg.The mean age of the 487 patients was 74 ± 9 years, and 57% were males. The indexed aortic valve area was 0.42 ± 0.12 cm3.2According to the 2015 definition, there were 210 (43%) patients with PH, 66 (13%) of whom exhibited CpcPH, 116 (24%) IpcPH, and 28 (6%) precapillary PH, whereas 277 (57%) patients had no PH at all. In only one CpcPH patient, the diagnosis was based only on the DPG criterion, while all others displayed a PVR >3 WU. Patient characteristics and hemodynamics of patients with CpcPH, IpcPH, precapillary PH, and those without PH according to the 2015 definition are shown in Table 3.3The number of patients with PH by the 2018 definition and the difference with the 2015 definition was 221  and 11 respectively; 65  of whom exhibited CpcPH, 140  IpcPH, and 16  precapillary PH, whereas 266  patients had no PH at all. Patient characteristics and hemodynamics of patients with CpcPH, IpcPH, precapillary PH, and those without PH according to the 2018 definition are shown in Table 3.4Overall, 52 11%) patients out of 487 were reclassified after applying the 2018 definition instead of 2015 definition, that is, 23 patients from no PH to IpcPH, eight from no PH to precapillary PH, 20 from precapillary PH to no PH, and one patient from CpcPH to IpcPH . After a median post‐AVR follow‐up of 44 (31–62) months, 44 deaths had occurred. When applying the 2015 definition, CpcPH patients experienced a four‐fold higher mortality compared to those without PH (referent), whereas mortality did not significantly differ among patients with IpcPH, precapillary PH, and no PH at all Figure . In cont3.6The prognostic impact of the single hemodynamic parameters contributing to the PH definitions is shown in Figure 4This first study analyzing the impact of the 2018 PH definitionVery little data have been published so far to illustrate the new PH definition's potential implications.The setting of our study differed from these studies,One of the strengths of the current study is that this is the only available study in a group 2 PH setting examining hemodynamic data in relation to long‐term prognostic information. According to both the 20154.1First, the number of patients in the different PH groups was relatively small and too low to come to a definite conclusion regarding the new definition's prognostic impact. Although we found similar mortality in patients with PVR <2 WU and those with PVR 2–3 WU, it remains possible that the risk of death may be rising at PVR values less than 3 as shown for other populations.4.2In severe AS, the new PH definition diagnoses a higher number of patients as having IpcPH, even though their prognosis is similar to that observed in patients without any PH. Patients with true precapillary PH are more accurately identified by means of the 2018 proposal that includes a pulmonary vascular disease criterion defined as PVR ≥3WU, which is a strong predictor of poor prognosis.Figure S1 Indexed aortic valve area (AVAi), left ventricular ejection fraction (LVEF), and key hemodynamic parameters in patients with different reclassification scenarios and patients who were not reclassified. Error bars represent means and standard deviations. CO, cardiac output; CpcPH, combined pre and postcapillary pulmonary hypertension; IpcPH, isolated postcapillary pulmonary hypertension; mPAP, mean pulmonary artery pressure; mPAWP, mean pulmonary artery wedge pressure; PH, pulmonary hypertension; PVR, pulmonary vascular resistance. The scale is: mm2/m2, %, mmHg, Wood units*10, and l/min*10.Click here for additional data file.Figure S2 Kaplan Meier plots showing cumulative events  in patients with any pulmonary hypertension (PH) according to the 2015 (panel A) and the 2018 (panel B) definition. HR, hazard ratio; 95% CI, 95% confidence interval.Click here for additional data file.Figure S3 Kaplan Meier plots showing cumulative events  in patients who were reclassified and who were no. HR, hazard ratio; 95% CI, 95% confidence interval.Click here for additional data file.Table S1 Clinical characteristics, echocardiographic findings, and hemodynamics of the study population (n = 487) according to the 2015 definition.Click here for additional data file.


The pilot study demonstrates pretreatment QUS‐radiomics can predict the recurrence group with an accuracy of 75% in patients with node‐positive HNSCC.This prospective study was conducted to investigate the role of quantitative ultrasound (QUS) radiomics in predicting recurrence for patients with node‐positive head‐neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). The most prominent cervical lymph node (LN) was scanned with a clinical ultrasound device having central frequency of 6.5 MHz. Ultrasound radiofrequency data were processed to obtain 7 QUS parameters. Color‐coded parametric maps were generated based on individual QUS spectral features corresponding to each of the smaller units. A total of 31 (7 primary QUS and 24 texture) features were obtained before treatment. All patients were treated with radical RT and followed according to standard institutional practice. Recurrence  served as an endpoint. Three different machine learning classifiers with a set of maximally three features were used for model development and tested with leave‐one‐out cross‐validation for nonrecurrence and recurrence groups. Fifty‐one patients were included, with a median follow up of 38 months (range 7–64 months). Recurrence was observed in 17 patients. The best results were obtained using a k‐nearest neighbor (KNN) classifier with a sensitivity, specificity, accuracy, and an area under curve of 76%, 71%, 75%, and 0.74, respectively. All the three features selected for the KNN model were texture features. The KNN‐model‐predicted 3‐year recurrence‐free survival was 81% and 40% in the predicted no‐recurrence and predicted‐recurrence groups, respectively. (Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684. Quantitative ultrasound radiomics obtained before starting treatment was used to predict the outcomes in patients with head‐neck squamous cell carcinoma treated with radical radiotherapy. A KNN classifier was able to predict the recurrence with an accuracy of 75%. Ultrasound is a simple, inexpensive, easily accessible portable imaging modality with rapid scan acquisition and excellent patient compliance. B‐mode US has a well‐established role in HNSCC, particularly in the determination of LN metastasis and is often undertaken to guide and obtain tissue diagnosis in suspicious sub‐clinical neck nodes.22.1The prospective observational study was undertaken at the Sunnybrook Health Sciences Centre, Toronto, approved by the institutional Research Ethics Committee. The trial was registered with clinicaltrials.gov.in (identifier NCT03908684). The study was conducted according to the declaration of Helsinki following good clinical practice and monitored by the institutional ethics committee. All patients were needed to have a diagnosis of biopsy‐proven HNSCC with primary site of oropharynx, hypopharynx, and larynx (inclusive of carcinoma of unknown primary with neck nodes) with clinically apparent neck nodes amenable to ultrasound imaging. Patients decided to be treated with radical intent RT (with or without concurrent chemotherapy), with no prior cancer‐directed therapies, were considered eligible for the study. Patients with evidence of distant metastasis were excluded from the study. A written consent form was obtained from all the patients. In patients with carcinoma unknown primary (CUP), the histology from LN showing features suggestive of nasopharyngeal carcinoma or expression of Epstein–Barr virus were excluded from the study. A minimum follow up of 12 months was considered for patients without any evidence of disease recurrence. Patients developing second primary in the head‐neck region or other areas  were excluded from the current study. The study accrual was done between January 2015 and June 2018. The survival data were locked for the final analysis in May 2020.2.2All the patients in the study were treated according to standard institutional protocols without any influence of the study. All the patients were treated with IMRT and IGRT techniques with a dose prescription of 70 Gy/33 fractions in 6–7 weeks to the high‐risk volume. The decision regarding concurrent systemic therapy was at the discretion of medical oncologists depending upon age, performance status, comorbidities following standard guidelines. Following treatment completion, the response was assessed at approximately 3 months with standard imaging (CT/ MRI) and clinical examination with endoscopy and functional imaging (PET) as decided by the treating oncologists. Patients with residual disease were closely followed up with additional investigations as indicated. Follow up was undertaken every 3–6 months in the initial 2 years and thereafter every 6–12 months. Disease recurrence was confirmed by the responsible clinicians with clinical examinations, imaging, and tissue diagnosis as appropriate. All the patients with suspected recurrent disease were discussed in a multidisciplinary tumor board constituting radiation oncologists, surgeons, medical oncologists, radiologists, and pathologists with expertise in head and neck malignancies. In general, for patients with local and nodal recurrence, endoscopic examination, cross‐sectional imaging, and biopsy were undertaken. Any patients with disease recurrence had undergone repeat staging investigations with CT neck, thorax, abdomen, pelvis with a bone scan or PET‐CT, and other symptom directed investigations (like MRI brain or spine). In general, for patients with disseminated metastatic disease, no additional histology was acquired, unless other differentials were considered .2.3The most prominent LN amenable to imaging was decided by the research sonographer in conjunction with the radiation oncologist. As a part of the study protocol, the index LN was required to have a size of more than 1 cm. The LN selected was ideally needed to be over an area accessible to ultrasound imaging . In general, the largest LN  was selected for imaging. Although the target LNs were not subjected to histological confirmation (unless CUP), the scanned LNs were included only if they had a strong radiological suspicion  of metastatic involvement as determined by radiation oncologists and radiologists and underwent treatment for disease involvement. Lymph nodes were included irrespective of the presence of radiological extranodal extension. The QUS scan was obtained before the start of RT . The ultrasound data were acquired using a clinical ultrasound system with standardized settings for ultrasound parameters  with a linear 4‐D transducer  or a Sonix RP clinical ultrasound system  with a linear array transducer (L14‐5/60). The center frequency was 6.5 MHz (bandwidth 3–8 MHz). The sampling frequency for both the device was 40 MHz, and the focal depth was 2.5 and 1.75 cm, respectively.The target LN was manually contoured designated as the region of interest (ROI). For each ROI, 3–5 slices were obtained at regular intervals encompassing the entire LN. The RF data collected from the ROI were divided into smaller blocks using a sliding window technique with a 92% overlap along the axial and lateral directions, corresponding to linear dimensions of approximately 2 mm × 2 mm. A fast Fourier transformation was applied to the raw RF data from each unit to generate the power spectrum, which was normalized using a tissue‐mimicking phantom serving as a reference. Seven QUS spectral parameters were obtained from the RF data‐spectral slope (SS), spectral intercept (SI) at 0 MHz, mid‐band fit (MBF), average acoustic concentration (AAC), average scatterer diameter (ASD), attenuation coefficient estimate (ACE), and spacing among scatterers (SAS). The spectral parameters obtained from each of the smaller units from all the slices were averaged and served as first‐order features. The details of image processing, standardization of parameters, texture feature extraction had been described in previous publications.For texture analysis, the color‐coded QUS‐parametric maps were generated using the quantitative estimates from each of the smaller units for individual spectral parameters (except ACE). A grey level co‐occurrence matrix (GLCM) method was used to generate texture features to compute the relation with neighboring pixels  for angular relations of 0°, 45°, 90° and 135°. Four texture features were extracted‐energy (ENE), contrast (CON), homogeneity (HOM), and correlation (COR). Therefore, a total of 24 QUS‐texture features were obtained from six spectral features leading to a total of 31 features. We used the weighted average  for all the individual spectral and texture parameters to generate a single value for concerned parameters. The individual features extracted from all the patients were normalized using a “Z‐score normalization” technique.2.40 to 210). A sequential forward feature selection method was used for data classification, using a maximum of three features to avoid overfitting of the model, given the smaller number of samples. The number of features was limited to 3, given the number of patients in the “no‐recurrence” group was 34, following the rule of thumb of using n/10 features for optimal classification. The feature selection method first analyses the best feature for classification and subsequently keeps adding the next set of features to the one already selected, to achieve the best classifier performance. As the number of subjects was unevenly distributed between the two groups, seven subsets were generated, selecting an equal proportion of patients from each of the groups in a random manner. The final classifier results were the values obtained from the combination of the individual subsets. The subset sampling involved downsampling the majority group (no‐recurrence) and training the algorithm. This process was run for seven iterations, with the final label (predicted recurrence vs. predicted no‐recurrence) decided through majority voting. Leave‐one‐out cross‐validation was performed to test the efficacy of the models and obtaining the confusion matrix. The method involves training the classifier algorithm with all subjects except one, which is used to test the algorithm. The process is repeated across the entire cohort until all the subjects are left out once. Receiver operating characteristics (ROC) was used to generate the area under curve (AUC) values. Kaplan–Meier product‐limit method was used for survival analysis. The date of the histopathological diagnosis of HNSCC was considered as the baseline date for survival analysis. The final influence of predicted groups from each of the classifiers (predicted recurrence vs. predicted no‐recurrence groups) on the recurrence‐free survival was tested using a log‐rank test. The segmentation, feature extraction, and machine learning classification were done using MATLAB . For statistical significance, a p‐value of <0.05 was considered significant.The endpoint (ground truth) for the study was recurrence and no‐recurrence as obtained from the clinical outcomes. A Shapiro–Wilk test was performed to study the distribution of the data between the two groups. Unpaired t‐tests were performed for normally distributed data, while Mann–Whitney tests were undertaken for nonparametric data. Three machine learning classifiers were used to develop the radiomics model‐Fisher's linear discriminant (FLD), k‐nearest neighbor (KNN), and support vector machine (SVM). The KNN classifier was used with different k values of 1, 2, 3, 4, and 5. For the SVM classifier, parameters C and γ were optimized using a grid search method , with 10 (20%) patients aged 70 years or above. The different patient, disease, and treatment characteristics are summarized in Table 3.2The median follow up for all patients in the study was 38 months (range 7 to 64 months). For patients without recurrence, the median follow up was 42 months (range 14 to 59 months). In the study here, 15 of 51 patients had a complete response in primary and lymph nodes at 3 months. Of the remaining 36 patients on further follow‐up, the disease had resolved in 28 patients, with a median time of 6 months (range 4–9 months). In all eight patients with residual disease, disease progression was seen at different times. The recurrence‐free survival (RFS) at 2 and 5‐year was 72% and 62%, respectively. A total of 17 patients had disease recurrences during the study period. The predominant site of relapse involved distant sites in 13 patients, regional nodes in 8, and local sites of primary disease in 4 , as shown in Figure S1. The median time to disease recurrence (from diagnosis) was 9 months (range 1 to 48 months), with more than 80% recurrences encountered in the initial 2 years. The 2 and 5‐year overall survival (OS) for all patients was 90% and 64%, respectively.3.3p‐values of 0.049 and 0.026, respectively. The scatter plots showing the distribution of all features between the two groups are presented in Figure Representative B‐mode and the QUS parametric maps are presented in Figure a priori to treatment predicting recurrence . Similarly, for the SVM‐model, the predicted 3‐year RFS was 44% and 83% for the predicted recurrence and predicted nonrecurrence groups, respectively (p < 0.001).The RFS, using the three model‐based predictions, has been shown in Figure 4Head and neck malignancies encompass a diverse group of cancers involving the head‐neck anatomical region, with the majority arising from the epithelial lining of the upper aerodigestive system or the various glandular structures . For HNSCC, the outcomes vary widely, with excellent cure rates seen in very early‐stage cancers, while advanced and metastatic tumors are associated with guarded prognosis.Several distinct risk factors  and molecular pathways  have been identified in the etiopathogenesis of HNSCC.In the study here, best results were obtained using a KNN‐based QUS model with an accuracy of 75% and AUC of 0.74, which is comparable to several previously reported studies. QUS imaging has been widely studied in breast cancer and established its efficacy in demarcating between different tumor grades, response prediction and the monitoring of response during treatment.Texture features determined information related to tumor heterogeneity, which is known to influence the clinical outcomes. Intra‐tumoral heterogeneity was evident within different areas of the tumoral masses, which can lead to the development of treatment resistance and impact survival.a priori to treatment as an imaging modality in predicting recurrence  in patients with HNSCC. This can be adopted in clinical practice and utilized to take a step toward personalizing treatments with intensification strategies for higher risk patients predicted to have a recurrence. One of the limitations of the current report is a relatively small number of patients; however, the results point to being able to predict tumor behavior and its impact on survival using quantitative ultrasound methods. In the future, with the expansion of study cohorts, it should be possible to use advanced classifiers like deep learning and test the effectiveness of the model in independent groups to increase reliability and clinical utility.This is the first study demonstrating the potential of QUS‐based radiomics obtained 5The study presented here demonstrates the effectiveness of a pretreatment QUS‐radiomics model in predicting recurrence for patients with HNSCC treated with radical RT with reasonable accuracy. A KNN‐based model provided the best classifier results with an accuracy of 75% and an AUC of 0.74.None to declare.Archya Dasgupta: Study concept, investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Kashuf Fatima: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Daniel DiCenzo: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Divya Bhardwaj: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Karina Quiaoit: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Murtuza Saifuddin: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Irene Karam: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Ian Poon: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Zain Husain: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. William T. Tran: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Lakshmanan Sannachi: Investigation, resources, formal analysis, methodology, writing‐original draft, review, editing. Gregory J. Czarnota: Study concept, investigation, funding, project administration, resources, formal analysis, methodology, writing‐original draft, review, editing.Terry Fox Foundation Program Project Grant from the Hecht Foundation (grant number 1083).Fig S1Click here for additional data file.Table S1Click here for additional data file.