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Objectives: Differentiating true progression or recurrence (TP/TR) from therapy-related changes (TRC) is complex in brain tumours. Amide proton transfer-weighted (APT) imaging is a chemical exchange saturation transfer (CEST) MRI technique that may improve diagnostic accuracy during radiological follow-up. This systematic review and meta-analysis elucidated the level of evidence and details of state-of-the-art imaging for APT-CEST in glioma and brain metastasis surveillance. Methods: PubMed, EMBASE, Web of Science, and Cochrane Library were systematically searched for original articles about glioma and metastasis patients who received APT-CEST imaging for suspected TP/TR within 2 years after (chemo)radiotherapy completion. Modified Quality Assessment of Diagnostic Accuracy Studies-2 criteria were applied. A meta-analysis was performed to pool results and to compare subgroups. Results: Fifteen studies were included for a narrative synthesis, twelve of which (500 patients) were deemed sufficiently homogeneous for a meta-analysis. Magnetisation transfer ratio asymmetry performed well in gliomas (sensitivity 0.88 [0.82–0.92], specificity 0.84 [0.72–0.91]) but not in metastases (sensitivity 0.64 [0.38–0.84], specificity 0.56 [0.33–0.77]). APT-CEST combined with conventional/advanced MRI rendered 0.92 [0.86–0.96] and 0.88 [0.72–0.95] in gliomas. Tumour type, TR prevalence, sex, and acquisition protocol were sources of significant inter-study heterogeneity in sensitivity (I2 = 62.25%; p < 0.01) and specificity (I2 = 66.31%; p < 0.001). Conclusion: A growing body of literature suggests that APT-CEST is a promising technique for improving the discrimination of TP/TR from TRC in gliomas, with limited data on metastases. Clinical relevance statement: This meta-analysis identified a utility for APT-CEST imaging regarding the non-invasive discrimination of brain tumour progression from therapy-related changes, providing a critical evaluation of sequence parameters and cut-off values, which can be used to improve response assessment and patient outcome. Key Points: Therapy-related changes mimicking progression complicate brain tumour treatment. Amide proton imaging improves the non-invasive discrimination of glioma progression from therapy-related changes. Magnetisation transfer ratio asymmetry measurement seems not to have added value in brain metastases. [ABSTRACT FROM AUTHOR]

Despite the success of biological therapies in treating inflammatory bowel disease, managing patients remains challenging due to the absence of reliable predictors of therapy response. In this study, we prospectively sampled 2 cohorts of patients with inflammatory bowel disease receiving the anti-integrin α4β7 antibody vedolizumab. Samples were subjected to mass cytometry; single-cell RNA sequencing; single-cell B and T cell receptor sequencing (BCR/TCR-seq); serum proteomics; and multiparametric flow cytometry to comprehensively assess vedolizumab-induced immunologic changes in the peripheral blood and their potential associations with treatment response. Vedolizumab treatment led to substantial alterations in the abundance of circulating immune cell lineages and modified the T-cell receptor diversity of gut-homing CD4+ memory T cells. Through integration of multimodal parameters and machine learning, we identified a significant increase in proliferating CD4+ memory T cells among nonresponders before treatment compared with responders. This predictive T-cell signature demonstrated an activated T-helper 1/T-helper 17 cell phenotype and exhibited elevated levels of integrin α4β1, potentially making these cells less susceptible to direct targeting by vedolizumab. These findings provide a reliable predictive classifier with significant implications for personalized inflammatory bowel disease management. [Display omitted] Despite the success of the anti-integrin antibody vedolizumab, few patients experience a sustained response. A comprehensive framework of nonresponse features is provided by harnessing multi-omics technologies and machine learning. [ABSTRACT FROM AUTHOR]

Background: The efficacy of a second radioactive iodine-131 (131I) treatment in patients with differentiated thyroid cancer (DTC) who did not achieve an excellent response (ER) following initial 131I therapy remains controversy and the population that would derive limited benefit from it is currently unclear. Objectives: The aim of this retrospective study was to assess the efficacy of the second 131I treatment in DTC patients with non-ER after the initial 131I therapy, and to identify potential risk factors associated with non-benefit of the second 131I treatment. Methods: 127 DTC patients who underwent two 131I treatments following thyroidectomy were included in this study, and the therapeutic response was evaluated after each 131I treatment. Beneficial treatment was defined as an improvement in therapy response grade (e.g. from indeterminate response to ER) after the second 131I treatment, while unbeneficial treatment was defined as no change or a downgrade in therapy response grade. The potential risk factors associated with the non-benefit of the second 131I treatment were identified using univariate and multivariate logistic regression models. Results: Following the second 131I treatment, therapy responses of 55.12% (70/127) of patients were reclassified to a better grade indicating treatment benefit, while 44.88% (57/127) showed no change or were reclassified to a worse grade suggesting no benefit from treatment. The non-benefit of the second 131I treatment was significantly associated with potential risk factors including stimulated thyroglobulin (sTg) level ≥ 11.46 ng/mL before the second 131I treatment, primary tumor size > 2 cm, status T2 or higher, N1b status and ATA high risk. Conclusions: The study results demonstrated that more than half of DTC patients could potentially benefit from a second 131I therapy. However, over 40% of patients exhibited no benefit in response to the second 131I treatment, suggesting potential overtreatment for this subgroup. Therefore, clinicians should exercise meticulous and precise decision-making based on identified risk factors when considering the necessity of a second 131I treatment. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is a highly lethal subtype of renal cancer. Accumulating evidence suggests cellular senescence impacts tumor development and progression. This study aimed to identify ccRCC subtypes based on a cellular senescence gene signature and assess their clinical relevance. Using hierarchical clustering on the TCGA-KIRC dataset, two senescence-related subtypes were identified and validated in independent datasets. These subtypes exhibited distinct dysregulation of cancer-related pathways, including the p53 pathway. The C2 subtype was associated with poorer overall survival, higher tumor grade and stage, low hemoglobin, and elevated platelet and serum calcium levels. Patients with the C2 subtype also had lower endothelial cell infiltration, indicating reduced benefit from anti-PD-1 immunotherapy. A nomogram based on these subtypes effectively predicted 1-, 3-, and 5-year survival outcomes. These findings highlight two distinct senescence-related ccRCC subtypes that correlate with prognosis and therapy response, offering insights for personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Quantitative dynamic contrast-enhanced (DCE) MRI as a promising method for the prediction of breast cancer response to neoadjuvant chemotherapy (NAC) has been demonstrated mostly in single-center and single-vendor platform studies. This preliminary study reports the initial experience in implementing quantitative breast DCE-MRI in multi-center (MC) and multi-vendor platform (MP) settings to predict NAC response. MRI data, including B1 mapping, variable flip angle (VFA) measurements of native tissue R1 (R1,0), and DCE-MRI, were acquired during NAC at three sites using 3T systems with Siemens, Philips, and GE platforms, respectively. High spatiotemporal resolution DCE-MRI was performed using similar vendor product sequences with k-space undersampling during acquisition and view sharing during reconstruction. A breast phantom was used for quality assurance/quality control (QA/QC) across sites. The Tofts model (TM) and shutter-speed model (SSM) were used for pharmacokinetic (PK) analysis of the DCE data. Additionally, tumor region of interest (ROI)- vs. voxel-based analyses in combination with the use of VFA-measured R1,0 vs. fixed, literature-reported R1,0 were investigated to determine the optimal analysis approach. Results from 15 patients who completed the study are reported. Voxel-based PK analysis using fixed R1,0 was deemed the optimal approach, which allowed the inclusion of data from one vendor platform where VFA measurements produced ≥100% overestimation of R1,0. The semi-quantitative signal enhancement ratio (SER) and quantitative PK parameters outperformed the tumor longest diameter (LD) in the prediction of pathologic complete response (pCR) vs. non-pCR after the first NAC cycle, whereas Ktrans consistently provided more accurate predictions than both SER and LD after the first NAC cycle and at the NAC midpoint. Both TM and SSM Ktrans and kep were excellent predictors of response at the NAC midpoint with ROC AUC >0.90, while the SSM parameters (AUC ≥0.80) performed better than their TM counterparts (AUC <0.80) after the first NAC cycle. The initial experience of this ongoing study indicates the importance of QA/QC using a phantom and suggests that deploying voxel-based PK analysis using a fixed R1,0 may mitigate random errors from R1,0 measurements across platforms and potentially eliminate the need for B1 and VFA acquisitions in MC and MP trials. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) can remain asymptomatic for years, complicating early detection and effective intervention. Predictive, Preventive, and Personalized Medicine (3PM) provides a transformative framework for addressing these challenges by integrating novel biomarkers, targeted prevention, and individualized therapies. Recent studies highlight the pivotal role of dysregulated microRNAs (miRNAs) and epigenetic alterations in cancer progression and metastasis. miRNAs, as non-coding RNAs approximately 22 nucleotides in length, regulate gene expression through translational inhibition or mRNA degradation. Dysregulated miRNAs are linked to the overexpression of oncogenic proteins and suppression of tumor suppressor genes in malignancies. Polyphenols such as curcumin, quercetin, resveratrol, and green tea catechins (EGCG) have demonstrated potential in modulating miRNA expression and reversing aberrant epigenetic modifications. Despite their established anticancer effects, the clinical application of polyphenols in stratified patient groups, particularly in primary and secondary cancer prevention, remains underexplored. Beyond their anti-inflammatory and antioxidant properties, polyphenols modulate early epigenetic and biogenetic events critical for cancer prevention and therapy. By targeting predictive biomarkers and improving therapy response, polyphenols contribute significantly to 3PM by enabling early diagnostics, mitigating risks, and personalizing treatments. This review evaluates current knowledge of polyphenols' impact on miRNAs and epigenetics in PCa and explores their potential applications within the 3PM framework, emphasizing predictive diagnostics, targeted prevention, and personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Abstract Background Emotional disorders such as depression and anxiety disorders share substantial similarities in their etiology and treatment. In recent decades, these commonalities have been increasingly recognized in classification systems and treatment programs crossing diagnostic boundaries. Methods To examine the prospective effects of different transdiagnostic markers on relevant treatment outcomes, we plan to track a minimum of N = 200 patients with emotional disorders during their routine course of cognitive behavioral therapy at two German outpatient clinics. We will collect a wide range of transdiagnostic markers, ranging from basic perceptual processes and self-report measures to complex behavioral and neurobiological indicators, before entering therapy. Symptoms and psychopathological processes will be recorded before entering therapy, between the 20th and 24th therapy session, and at the end of therapy. Discussion Our results could help to identify transdiagnostic markers with high predictive power, but also provide deeper insights into which patient groups with which symptom clusters are less likely to benefit from therapy, and for what reasons. Trial Registration The trial was preregistered at the German Clinical Trial Register (DRKS-ID: DRKS00031206; 2023–05-09).

Background: Emotional disorders such as depression and anxiety disorders share substantial similarities in their etiology and treatment. In recent decades, these commonalities have been increasingly recognized in classification systems and treatment programs crossing diagnostic boundaries. Methods: To examine the prospective effects of different transdiagnostic markers on relevant treatment outcomes, we plan to track a minimum of N = 200 patients with emotional disorders during their routine course of cognitive behavioral therapy at two German outpatient clinics. We will collect a wide range of transdiagnostic markers, ranging from basic perceptual processes and self-report measures to complex behavioral and neurobiological indicators, before entering therapy. Symptoms and psychopathological processes will be recorded before entering therapy, between the 20th and 24th therapy session, and at the end of therapy. Discussion: Our results could help to identify transdiagnostic markers with high predictive power, but also provide deeper insights into which patient groups with which symptom clusters are less likely to benefit from therapy, and for what reasons. Trial Registration: The trial was preregistered at the German Clinical Trial Register (DRKS-ID: DRKS00031206; 2023–05-09). [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is the second leading cause of male cancer-related deaths in Western countries, which is predominantly attributed to the metastatic castration-resistant stage of the disease (CRPC). There is an urgent need for better prognostic and predictive biomarkers, particularly for androgen receptor targeted agents and taxanes. Methods: We have searched the PubMed database for original articles and metaanalyses providing information on blood-based markers for castration-resistant prostate cancer monitoring, risk group stratification and prediction of therapy response. Results: The molecular markers are discussed along with the standard clinical parameters, such as prostate specific antigen, lactate dehydrogenase or Creactive protein. Androgen receptor (AR) alterations are commonly associated with progression to CRPC. These include amplification of AR and its enhancer, point mutations and splice variants. Among DNA methylations, a novel 5-hydroxymethylcytosine activation marker of TOP2A and EZH2 has been identified for the aggressive disease. miR-375 is currently the most promising candidate among non-coding RNAs and sphingolipid analysis has recently emerged as a novel approach. Conclusions: The promising biomarkers have the potential to improve the care of metastatic prostate cancer patients, however, they need further validation for routine implementation. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the reliability of the Deauville score (DS) in therapy response assessment and to define the prognostic value of the metabolic response of end of induction (EOI) [18F]FDG PET (PET) in follicular lymphoma patients. Methods: Adult patients with untreated grade 1–3a FL/ stage II‐IV enrolled in the multicentre, prospective, phase III FOLL12 trial (NCT02063685) were randomized to receive standard immunochemotherapy followed by rituximab maintenance (standard arm) versus standard immunochemotherapy followed by response-adapted post‐induction management (experimental arm). Baseline and EOI PET were mandatory for the study. All PET scans were centralized on the WIDEN® platform and classified according to DS in a blind independent central review. DS1–3 was considered negative (CMR), whereas DS4‐5 was considered positive (not CMR). The primary endpoint was PFS. The main secondary endpoint was overall survival (OS). Results: Overall, 807 follicular lymphoma patients—52% women, 89% stage III-IV disease, 40% with a high-risk FLIPI-2 score (3–5)—were enrolled in the study; 729 (90.4%) baseline and EOI PET were available for the analysis. EOI PET was positive (DS4–5) in 88/729 (12.1%) cases. Overall inter-reviewer agreement on PET pos/neg result was 0.92, while agreement on positive and negative cases was 0.77 and 0.94, respectively. The median follow-up was 69 months; 247 events were registered in the 5-yr follow-up, with a 5-yr PFS of 67% (95%CI: 63%–70%). The 5-yr PFS rate for PET neg (DS1–3) and PET pos (DS4–5) patients was 71% (95%CI: 67%–75%) and 36% (95%CI: 25%–46%), respectively, with HR 3.49 (95%CI: 2.57–4.72). Five-year PFS was worse as DS increased, with 74% (70%–78%), 58% (48%–67%; HR 1.71; p = 0.001)] and 36% (25%–46%; HR 3.88; p < 0.001) in DS1–2, DS3 and DS4–5, respectively. EOI PET maintained its prognostic value in both the standard and experimental arms. In the whole population, 5-yr OS was 94% (95%CI: 92%–96%), with 96% (95%CI: 94–97) and 82% (95%CI: 72%–89%) in EOI PET negative (DS1–3) and positive (DS4–5), respectively (HR 4.48; p < 0.001). When DS was associated with FLIPI-2, patients with DS3 or DS1–2 with high FLIPI-2 (3–5) experienced worse OS than patients with DS1–2 and low FLIPI-2 (1–2) (p = 0.003). Conclusion: This study shows that DS is a reliable prognostic tool to evaluate EOI PET in follicular lymphoma patients, with prognostic value maintained both in the standard and experimental arms, making metabolic imaging a robust tool to assess response in FL. Moreover, although preliminary, this study provides further information on DS3 patients, who are considered as CMR but show a less favourable PFS than DS1–2 patients. [ABSTRACT FROM AUTHOR]

Rationale: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment. Objective: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy. Methods: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy. Results: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98. Conclusion: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.

ObjectivesThis pilot study aimed to identify early predictors of drug retention in patients with clinically active peripheral psoriatic arthritis who initiated or switched to therapy with biologic and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs).MethodsClinical and ultrasound assessments were conducted at baseline (t0) and subsequently at 1 (t1), 3 (t3), and 6 (t6) months. Ultrasound evaluations targeted joints/entheses according to PsASon-Score13 and the most clinically involved joint/enthesis/tendon or the two most clinically involved joints/entheses/tendons (MIJET and 2MIJET). After 6 months of follow-up, patients were divided into two groups based on drug retention, determined by the clinician's assessment of treatment efficacy (cResponder vs. non-cResponder). Main endpoints were ultrasound changes in MIJET, 2MIJET, and GUIS (Global US Inflammation Subscore) derived from PsASon-13.ResultsTwenty-nine patients were enrolled, 22 cResponders and 7 non-cResponders at t6. In the comparison between cResponders and non-cResponders, GUIS variation significantly differed in Δt6-t0, while MIJET and 2MIJET variations were significant as early as Δt3-t0 and confirmed in Δt6-t0. The ultrasound response of MIJET and 2MIJET was faster in cResponders treated with JAKi vs. those treated with TNFi and IL-17/12-23i, significant in Δt1-t0.ConclusionsUltrasound imaging of clinically involved joint sites may be a valuable early predictor of therapy response for predicting drug retention at 6 months in patients with psoriatic arthritis.

Abstract Background Trophinin Associated Protein (TROAP) has been implicated in some tumors, yet its role in renal cell carcinoma (RCC) remains underexplored. This study aims to elucidate the prognostic and therapeutic implications of TROAP in RCC, encompassing different subtypes. Methods Firstly, we identified the expression patterns of TROAP across various tumors within the TCGA pan-cancer cohort. Subsequently, the prognostic significance of TROAP was validated in three TCGA RCC cohorts and a local cohort. Finally, we conducted functional enrichment analysis, somatic mutations and copy number variations, assessed therapeutic response cohorts, and performed in vitro experiments to explore the biological characteristics of TROAP. Results TROAP serves as an unfavorable factor in both the TCGA RCC datasets and our local cohort. Functional enrichment analysis and in vitro experiments have demonstrated its oncogene effect in promoting tumor progression. Additionally, the relationship between TROAP expression and gene mutations in RCC appears to be limited. Furthermore, elevated TROAP expression is associated with reduced efficacy of RCC therapies, including nivolumab and everolimus. Conclusions Our findings illustrate TROAP as a pivotal biomarker for prognosis and therapeutic response in RCC. Elevated TROAP expression is indicative of aggressive tumor behavior and resistance to conventional therapies, making it a valuable target for personalized treatment strategies in RCC management.

Abstract The tumor microenvironment (TME) plays a pivotal role in the onset, progression, and treatment response of cancer. Among the various components of the TME, cancer-associated fibroblasts (CAFs) are key regulators of both immune and non-immune cellular functions. Leveraging single-cell RNA sequencing (scRNA) data, we have uncovered previously hidden and promising roles within this specific CAF subgroup, paving the way for its clinical application. However, several critical questions persist, primarily stemming from the heterogeneous nature of CAFs and the use of different fibroblast markers in various sample analyses, causing confusion and hindrance in their clinical implementation. In this groundbreaking study, we have systematically screened multiple databases to identify the most robust marker for distinguishing CAFs in lung cancer, with a particular focus on their potential use in early diagnosis, staging, and treatment response evaluation. Our investigation revealed that COL1A1, COL1A2, FAP, and PDGFRA are effective markers for characterizing CAF subgroups in most lung adenocarcinoma datasets. Through comprehensive analysis of treatment responses, we determined that COL1A1 stands out as the most effective indicator among all CAF markers. COL1A1 not only deciphers the TME signatures related to CAFs but also demonstrates a highly sensitive and specific correlation with treatment responses and multiple survival outcomes. For the first time, we have unveiled the distinct roles played by clusters of CAF markers in differentiating various TME groups. Our findings confirm the sensitive and unique contributions of CAFs to the responses of multiple lung cancer therapies. These insights significantly enhance our understanding of TME functions and drive the translational application of extensive scRNA sequence results. COL1A1 emerges as the most sensitive and specific marker for defining CAF subgroups in scRNA analysis. The CAF ratios represented by COL1A1 can potentially serve as a reliable predictor of treatment responses in clinical practice, thus providing valuable insights into the influential roles of TME components. This research marks a crucial step forward in revolutionizing our approach to cancer diagnosis and treatment.

Background: Trophinin Associated Protein (TROAP) has been implicated in some tumors, yet its role in renal cell carcinoma (RCC) remains underexplored. This study aims to elucidate the prognostic and therapeutic implications of TROAP in RCC, encompassing different subtypes. Methods: Firstly, we identified the expression patterns of TROAP across various tumors within the TCGA pan-cancer cohort. Subsequently, the prognostic significance of TROAP was validated in three TCGA RCC cohorts and a local cohort. Finally, we conducted functional enrichment analysis, somatic mutations and copy number variations, assessed therapeutic response cohorts, and performed in vitro experiments to explore the biological characteristics of TROAP. Results: TROAP serves as an unfavorable factor in both the TCGA RCC datasets and our local cohort. Functional enrichment analysis and in vitro experiments have demonstrated its oncogene effect in promoting tumor progression. Additionally, the relationship between TROAP expression and gene mutations in RCC appears to be limited. Furthermore, elevated TROAP expression is associated with reduced efficacy of RCC therapies, including nivolumab and everolimus. Conclusions: Our findings illustrate TROAP as a pivotal biomarker for prognosis and therapeutic response in RCC. Elevated TROAP expression is indicative of aggressive tumor behavior and resistance to conventional therapies, making it a valuable target for personalized treatment strategies in RCC management. [ABSTRACT FROM AUTHOR]

Objective: Cognitive Behavior Therapy (CBT) is an effective treatment for anxiety and depression disorders. Nonetheless, nearly 50% of all patients do not respond. Besides other factors, nonresponse may be linked to traumatic life events. This study aims to assess the relationship between trauma history, applied therapy interventions, and therapy outcomes. Methods: We analyzed data from 340 CBT outpatients diagnosed with a depression or anxiety disorder and possibly a trauma history treated under naturalistic conditions. Based on their therapy files, we collected information on trauma history, diagnoses, applied interventions, and severity of depression and anxiety symptoms at the start and end of therapy. The relationship between trauma, diagnoses, and intervention use and the development of depression and anxiety symptoms was analyzed using Linear Mixed Models. Results: Patients with a trauma history reported higher pre‐ and posttreatment symptom severity than those without trauma. No differences in applied interventions or decrease in symptom severity were found between patients with and without a trauma history. Specialized interventions were seldom applied. Conclusion: Although no differences between patients with and without a trauma history were found in therapy response, patients with a trauma history maintained higher levels of symptom severity. These results indicate a need for more personalized interventions and evidence‐based guidelines to personalize CBT for patients with a trauma history and high symptom severity. [ABSTRACT FROM AUTHOR]

The tumor microenvironment (TME) plays a pivotal role in the onset, progression, and treatment response of cancer. Among the various components of the TME, cancer-associated fibroblasts (CAFs) are key regulators of both immune and non-immune cellular functions. Leveraging single-cell RNA sequencing (scRNA) data, we have uncovered previously hidden and promising roles within this specific CAF subgroup, paving the way for its clinical application. However, several critical questions persist, primarily stemming from the heterogeneous nature of CAFs and the use of different fibroblast markers in various sample analyses, causing confusion and hindrance in their clinical implementation. In this groundbreaking study, we have systematically screened multiple databases to identify the most robust marker for distinguishing CAFs in lung cancer, with a particular focus on their potential use in early diagnosis, staging, and treatment response evaluation. Our investigation revealed that COL1A1, COL1A2, FAP, and PDGFRA are effective markers for characterizing CAF subgroups in most lung adenocarcinoma datasets. Through comprehensive analysis of treatment responses, we determined that COL1A1 stands out as the most effective indicator among all CAF markers. COL1A1 not only deciphers the TME signatures related to CAFs but also demonstrates a highly sensitive and specific correlation with treatment responses and multiple survival outcomes. For the first time, we have unveiled the distinct roles played by clusters of CAF markers in differentiating various TME groups. Our findings confirm the sensitive and unique contributions of CAFs to the responses of multiple lung cancer therapies. These insights significantly enhance our understanding of TME functions and drive the translational application of extensive scRNA sequence results. COL1A1 emerges as the most sensitive and specific marker for defining CAF subgroups in scRNA analysis. The CAF ratios represented by COL1A1 can potentially serve as a reliable predictor of treatment responses in clinical practice, thus providing valuable insights into the influential roles of TME components. This research marks a crucial step forward in revolutionizing our approach to cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Objective: Antigen-presenting dendritic cells (DCs) and monocytes play an essential role in rheumatoid arthritis (RA) pathogenesis, however, their tolerogenic potential remains unclear. Herein, the tolerogenic profiles of DCs are characterized in treatment-naïve RA patients to determine their role to inflammatory arthritis management. Methods: Thirty-six treatment-naïve RA patients were enrolled, of which 62% were non-responders to methotrexate (MTX) monotherapy based on disease activity score (DAS) after 6-months of therapy. DC and monocyte subset frequencies, activation (CD40, CD86, CD209 expression), and tolerogenic profile (intracellular indoleamine-2,3-dioxygenase [IDO1] and cytotoxic T lymphocyte antigen 4 [CTLA-4] expression) were examined in the baseline peripheral blood by multicolor flow-cytometry. Soluble CTLA-4 (sCTLA-4) levels in plasma were measured. Results: DC subsets were decreased in RA compared to healthy controls (HC), and the frequency of conventional DCs (cDC) inversely correlated with inflammatory markers and improvement in disease activity. CD141+ cDC1s were the major IDO1-expressing cells. IDO1+cDC1s were reduced in RA patients compared to HC. The baseline frequency of IDO1+cDC1s inversely correlated with improvement in disease activity. CTLA-4 expression in CD1c+ cDC2s and monocytes was lower in RA patients compared to HC. Moreover, MTX-responders had a significantly lower frequency of IDO1+cDC1 cells and higher level of sCTLA-4 in the plasma compared to MTX non-responders. There was a strong predictive association of low IDO1+cDC1 cells, low sCTLA-4 and non-response to MTX. Conclusions: Our findings reveal altered DC and monocytes immunophenotypes that are associated with RA pathology and treatment response. The frequencies of tolerogenic IDO1+cDC1s and the low level of sCTLA-4 are strongly associated with MTX non-responsiveness and therapeutic outcome. These results suggest that investigation of the association IDO1+cDC1 and sCTLA-4 with response to treatment may be more generalizable to other autoimmune diseases. [ABSTRACT FROM AUTHOR]

Quantitative dynamic contrast-enhanced (DCE) MRI as a promising method for the prediction of breast cancer response to neoadjuvant chemotherapy (NAC) has been demonstrated mostly in single-center and single-vendor platform studies. This preliminary study reports the initial experience in implementing quantitative breast DCE-MRI in multi-center (MC) and multi-vendor platform (MP) settings to predict NAC response. MRI data, including B1 mapping, variable flip angle (VFA) measurements of native tissue R1 (R1,0), and DCE-MRI, were acquired during NAC at three sites using 3T systems with Siemens, Philips, and GE platforms, respectively. High spatiotemporal resolution DCE-MRI was performed using similar vendor product sequences with k-space undersampling during acquisition and view sharing during reconstruction. A breast phantom was used for quality assurance/quality control (QA/QC) across sites. The Tofts model (TM) and shutter-speed model (SSM) were used for pharmacokinetic (PK) analysis of the DCE data. Additionally, tumor region of interest (ROI)- vs. voxel-based analyses in combination with the use of VFA-measured R1,0vs. fixed, literature-reported R1,0 were investigated to determine the optimal analysis approach. Results from 15 patients who completed the study are reported. Voxel-based PK analysis using fixed R1,0 was deemed the optimal approach, which allowed the inclusion of data from one vendor platform where VFA measurements produced ≥100% overestimation of R1,0. The semi-quantitative signal enhancement ratio (SER) and quantitative PK parameters outperformed the tumor longest diameter (LD) in the prediction of pathologic complete response (pCR) vs. non-pCR after the first NAC cycle, whereas Ktrans consistently provided more accurate predictions than both SER and LD after the first NAC cycle and at the NAC midpoint. Both TM and SSM Ktrans and kep were excellent predictors of response at the NAC midpoint with ROC AUC >0.90, while the SSM parameters (AUC ≥0.80) performed better than their TM counterparts (AUC

BackgroundProstate cancer is the second leading cause of male cancer-related deaths in Western countries, which is predominantly attributed to the metastatic castration-resistant stage of the disease (CRPC). There is an urgent need for better prognostic and predictive biomarkers, particularly for androgen receptor targeted agents and taxanes.MethodsWe have searched the PubMed database for original articles and meta-analyses providing information on blood-based markers for castration-resistant prostate cancer monitoring, risk group stratification and prediction of therapy response.ResultsThe molecular markers are discussed along with the standard clinical parameters, such as prostate specific antigen, lactate dehydrogenase or C-reactive protein. Androgen receptor (AR) alterations are commonly associated with progression to CRPC. These include amplification of AR and its enhancer, point mutations and splice variants. Among DNA methylations, a novel 5-hydroxymethylcytosine activation marker of TOP2A and EZH2 has been identified for the aggressive disease. miR-375 is currently the most promising candidate among non-coding RNAs and sphingolipid analysis has recently emerged as a novel approach.ConclusionsThe promising biomarkers have the potential to improve the care of metastatic prostate cancer patients, however, they need further validation for routine implementation.

Purpose: To evaluate if a machine learning prediction model based on clinical and easily assessable imaging features derived from baseline breast [18F]FDG-PET/MRI staging can predict pathologic complete response (pCR) in patients with newly diagnosed breast cancer prior to neoadjuvant system therapy (NAST). Methods: Altogether 143 women with newly diagnosed breast cancer (54 ± 12 years) were retrospectively enrolled. All women underwent a breast [18F]FDG-PET/MRI, a histopathological workup of their breast cancer lesions and evaluation of clinical data. Fifty-six features derived from positron emission tomography (PET), magnetic resonance imaging (MRI), sociodemographic / anthropometric, histopathologic as well as clinical data were generated and used as input for an extreme Gradient Boosting model (XGBoost) to predict pCR. The model was evaluated in a five-fold nested-cross-validation incorporating independent hyper-parameter tuning within the inner loops to reduce the risk of overoptimistic estimations. Diagnostic model-performance was assessed by determining the area under the curve of the receiver operating characteristics curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Furthermore, feature importances of the XGBoost model were evaluated to assess which features contributed most to distinguish between pCR and non-pCR. Results: Nested-cross-validation yielded a mean ROC-AUC of 80.4 ± 6.0% for prediction of pCR. Mean sensitivity, specificity, PPV, and NPV of 54.5 ± 21.3%, 83.6 ± 4.2%, 63.6 ± 8.5%, and 77.6 ± 8.1% could be achieved. Histopathological data were the most important features for classification of the XGBoost model followed by PET, MRI, and sociodemographic/anthropometric features. Conclusion: The evaluated multi-source XGBoost model shows promising results for reliably predicting pathological complete response in breast cancer patients prior to NAST. However, yielded performance is yet insufficient to be implemented in the clinical decision-making process. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) studies on major depressive disorder (MDD) have predominantly found short-term electroconvulsive therapy (ECT)-related gray matter volume (GMV) increases, but research on the long-term stability of such changes is missing. Our aim was to investigate long-term GMV changes over a 2-year period after ECT administration and their associations with clinical outcome. Methods: In this nonrandomized longitudinal study, patients with MDD undergoing ECT (n = 17) are assessed three times by structural MRI: Before ECT (t 0), after ECT (t 1) and 2 years later (t 2). A healthy (n = 21) and MDD non-ECT (n = 33) control group are also measured three times within an equivalent time interval. A 3(group) × 3(time) ANOVA on whole-brain level and correlation analyses with clinical outcome variables is performed. Results: Analyses yield a significant group × time interaction (pFWE < 0.001) resulting from significant volume increases from t 0 to t 1 and decreases from t 1 to t 2 in the ECT group, e.g., in limbic areas. There are no effects of time in both control groups. Volume increases from t 0 to t 1 correlate with immediate and delayed symptom increase, while volume decreases from t 1 to t 2 correlate with long-term depressive outcome (all p ⩽ 0.049). Conclusions: Volume increases induced by ECT appear to be a transient phenomenon as volume strongly decreased 2 years after ECT. Short-term volume increases are associated with less symptom improvement suggesting that the antidepressant effect of ECT is not due to volume changes. Larger volume decreases are associated with poorer long-term outcome highlighting the interplay between disease progression and structural changes. [ABSTRACT FROM AUTHOR]

Objective: The BRAFV600E mutation is the universal genetic mutation in papillary thyroid microcarcinoma (PTMC). The present study is to estimate the role of the BRAFV600E mutation in the clinical outcome of PTMC with intermediate to high recurrence risk after radioactive iodine (RAI) therapy, which is considered to be an indolent tumor. Methods: We conducted a single-center retrospective study. Between May 2016 and March 2019, PTMC patients with known BRAFV600E status who received RAI therapy were reviewed at the Second Hospital of Shandong University. Treatment and follow-up were defined according to criteria used in the 2015 ATA guidelines. The association between the BRAFV600E mutation and clinicopathological characteristics, response to RAI therapy, and recurrence after a period of follow-up were analyzed. Propensity score matching (PSM) and logistic regression were used to control confounding variables. Results: Of the 322 patients with intermediate to high recurrence risk in PTMC, the mean age of the patients were 43.7 ± 12.2 years, and 72.1% were women. BRAFV600E mutation was found in 64.9% (209/322). After PSM, 112 pairs of patients were matched, and except for multifocality (P = 0.001), extrathyroidal invasion (P = 0.003) and tumor size (P = 0.03), there was no significant difference in all baseline characteristics between the two groups. An excellent response (ER) to RAI therapy was observed in 273 patients (84.7%). At the end of the study, 17(5.2%) and 6(1.8%) patients showed structural incomplete response (SIR) and biochemical incomplete response (BIR) status. The proportion of patients who achieved ER status in the BRAFV600E mutation positive and negative groups was 86.6% and 81.4%, respectively. Kaplan–Meier analyses showed that the BRAFV600E mutation was not related to lower ER reached time. The median follow-up was 51 months. Conclusions: We found the BRAFV600E mutation was associated with multifocality, extrathyroidal invasion, and tumor size in papillary thyroid microcarcinoma. However, the BRAFV600E mutation had no significant association with clinical outcomes in patients with intermediate to high recurrence risk after RAI therapy. Furthermore, the extra-thyroid uptake results and distant metastasis had been proven to be independent factor predicting the clinical response. Registration number: ChiCTR2200062911 [ABSTRACT FROM AUTHOR]

Background: Virtual reality (VR) therapy is increasingly used and has shown encouraging effects. Yet, it is unknown which patients respond best to VR-based balance therapy as part of neurorehabilitation. Methods: Data from 30 persons with stroke and 51 persons with multiple sclerosis who performed three to four weeks of VR-based balance therapy during in-patient rehabilitation were analysed. Participants were divided into responders and nonresponders based on achievement of the minimal clinically important difference in functional balance post intervention. Measures of balance, trunk function, mobility, gait, motivation, and exergame parameters were compared between groups. Results: Post intervention, all clinical measurements significantly improved (p < 0.05; effect size: 0.45–0.59). Participants that achieved the minimal clinically important difference in functional balance (n = 49; 60%) had significantly lower preintervention functional and dynamic balance (median(IQR): 39(27–46) versus 45(37–50); p = 0.02 and 11(6–15) versus 16(11–18); p = 0.03). They spent less time on higher difficulty exercises (11(8–17) versus 14.5(10–12); p = 0.03) and demonstrated increased motivation over time compared with nonresponders (1(−1–5) versus −2(−7–3); p = 0.03). Conclusion: Lower baseline balance ability, spending more time on adequately challenging exercises, and increased motivation potentially influence response to therapy. These factors can support the personalisation of VR-based balance therapy. [ABSTRACT FROM AUTHOR]

The immune‐modulating protein CD52 attenuates lymphocyte function and is associated with autoimmune disorders, for example, multiple sclerosis (MS). CD52 represents a therapeutic target in MS and chronic lymphocytic leukemia (CLL). Its expression has prognostic and predictive value in CLL and is prognostic in breast cancer. Its significance in melanoma is unclear. We analyzed CD52 mRNA expression data from tumor bulk tissues of N = 445 untreated melanoma patients from The Cancer Genome Atlas (TCGA) Research Network and of N = 121 melanoma patients undergoing anti‐PD‐1 immune checkpoint blockade (ICB) with regard to outcome (overall survival [OS], disease control [DC], and progression‐free survival [PFS]), single‐cell RNA‐Seq data of N = 4645 cells from N = 19 melanoma tissues, and N = 15,457 cells from normal skin provided by N = 5 donors. Higher CD52 mRNA expression was associated with favorable OS (hazard ratio (HR) = 0.820, [95% CI 0.734–0.916], p <.001) in non‐ICB‐treated melanoma and with PFS (HR = 0.875, [95% CI 0.775–0.989], p =.033) and DC (p =.005) in ICB‐treated melanoma. CD52 expression correlated significantly with distinct immune cell subsets and correlated negatively with immune checkpoint expression in T cells. Moreover, our results suggest CD52 expression by a certain type of tissue‐resident macrophages. CD52 mRNA was expressed in a small subgroup (8%) of immune checkpoint coexpressing melanoma cells. CD52 expression is associated with features of ICB response in melanoma. Concomitant ICB and anti‐CD52 treatment requires critical review. [ABSTRACT FROM AUTHOR]

Recent advances in the field of immuno‐oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high‐throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi‐marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

ObjectiveAntigen-presenting dendritic cells (DCs) and monocytes play an essential role in rheumatoid arthritis (RA) pathogenesis, however, their tolerogenic potential remains unclear. Herein, the tolerogenic profiles of DCs are characterized in treatment-naïve RA patients to determine their role to inflammatory arthritis management.MethodsThirty-six treatment-naïve RA patients were enrolled, of which 62% were non-responders to methotrexate (MTX) monotherapy based on disease activity score (DAS) after 6-months of therapy. DC and monocyte subset frequencies, activation (CD40, CD86, CD209 expression), and tolerogenic profile (intracellular indoleamine-2,3-dioxygenase [IDO1] and cytotoxic T lymphocyte antigen 4 [CTLA-4] expression) were examined in the baseline peripheral blood by multicolor flow-cytometry. Soluble CTLA-4 (sCTLA-4) levels in plasma were measured.ResultsDC subsets were decreased in RA compared to healthy controls (HC), and the frequency of conventional DCs (cDC) inversely correlated with inflammatory markers and improvement in disease activity. CD141+ cDC1s were the major IDO1-expressing cells. IDO1+cDC1s were reduced in RA patients compared to HC. The baseline frequency of IDO1+cDC1s inversely correlated with improvement in disease activity. CTLA-4 expression in CD1c+ cDC2s and monocytes was lower in RA patients compared to HC. Moreover, MTX-responders had a significantly lower frequency of IDO1+cDC1 cells and higher level of sCTLA-4 in the plasma compared to MTX non-responders. There was a strong predictive association of low IDO1+cDC1 cells, low sCTLA-4 and non-response to MTX.ConclusionsOur findings reveal altered DC and monocytes immunophenotypes that are associated with RA pathology and treatment response. The frequencies of tolerogenic IDO1+cDC1s and the low level of sCTLA-4 are strongly associated with MTX non-responsiveness and therapeutic outcome. These results suggest that investigation of the association IDO1+cDC1 and sCTLA-4 with response to treatment may be more generalizable to other autoimmune diseases.

Objective: This retrospective study aims to evaluate the therapeutic effect of varying dosages of adjuvant radioactive iodine (RAI) therapy on intermediate-risk papillary thyroid carcinoma (PTC) patients. Methods: This retrospective study involved a total of 427 intermediate-risk PTC patients, out of which 202 received a 3.7GBq dosage of RAI, and 225 received a 5.55GBq dosage. The evaluation involved assessing the therapeutic outcomes, number of treatment cycles, and successful remnant ablation rates in both dose groups, six months post-adjuvant RAI therapy. Univariate and multivariate logistic regression analyses were employed to identify factors linked with excellent response (ER). Following this, prognostic nomograms were constructed to provide a visual representation of the prediction models. Calibration curves, the concordance index (C-index), and the receiver operating characteristic (ROC) curve were employed to evaluate the predictive performance of these nomograms. The Hosmer-Lemeshow test was applied to assess the models' goodness-of-fit. Additionally, the clinical utility of the prognostic nomograms was appraised through decision curve analysis (DCA). Results: The high-dose (HD) group exhibited significantly higher proportions of ER, single treatment cycles, and successful remnant ablation rates (p<0.05). Being male, receiving a 3.7GBq dose, having an N1b stage, an sTg level =10ng/ml, or an sTg/TSH ratio =0.11 were independent risk factors for Non-ER. Two prognostic nomograms, "sTg Nomogram" and "sTg/TSH Nomogram", were established. The ranking of factors contributing to ER, in descending order, included the sTg or sTg/TSH ratio, N stage, therapy dosage, sex, and soft tissue invasion. The "sTg/TSH Nomogram" demonstrated a higher C-index compared to the "sTg Nomogram". The calibration curves indicated excellent calibration for both nomograms. DCA demonstrated that the net benefit of the "sTg/TSH Nomogram" was higher than that of the "sTg Nomogram". Conclusion: Higher initial RAI therapy doses can improve therapeutic efficacy for intermediate-risk PTC patients. The developed nomograms, particularly the "sTg/TSH Nomogram", could assist clinicians in optimal therapeutic decision-making. [ABSTRACT FROM AUTHOR]

Simple Summary: The identification of prognostic markers in neoadjuvant therapy patients is critical for treatment optimisation. The purpose of our retrospective study was to determine the role of Dkk1 as a predictor of NACT response in BC patients. Dkk1 levels were found to be lower in treated BC tumours than in untreated tumours. The results of 68 matched pre- and post-therapy tissues showed that advanced G status and TNBC subtype were associated with a higher percentage of Dkk1 expression reduction. In addition, decreased Dkk1-IRS in core needle biopsy tissues independently predicted regression grade (R4), according to Sinn. Dkk1 could then be identified as a biomarker for personalised neoadjuvant therapy. Purpose: To investigate the role of Dkk1 as a predictor of response to NACT in BC patients. Methods: This retrospective monocentric study included 145 women who had undergone NACT followed by breast surgery. Dkk1 protein expression was assessed using immunohistochemistry staining in core needle biopsies and mammary carcinoma specimens. Results: Dkk1 levels were lower in treated BC tumours than in untreated tumours. The outcomes of 68 matched pre- and post-therapy tissues showed that Dkk1 levels in mammary carcinoma tissues were significantly predicted by levels in core needle biopsies and that Dkk1 expression was reduced in 83% of cases. Smaller cT stage, positive Her2 expression, and decreased Dkk1-IRS in core needle biopsy tissues were all independent predictors of regression grade (R4), according to Sinn. However, the percentage of Dkk1 expression differences prior to and following NACT had no effect on PFS or OS. Conclusions: In this study, we demonstrated for the first time that Dkk1 could be identified as an independent predictor of NACT response in BC patients, particularly those with TNBC. Further research with a multicentric expanded (pre-/post-therapy) sample set and better-defined populations in terms of molecular subtypes, therapy modality, and long-term follow-up is recommended to obtain more solid evidence. [ABSTRACT FROM AUTHOR]

Objectives: Because size-based imaging criteria poorly capture biologic response in desmoid-type fibromatosis (DF), changes in MRI T2 signal intensity are frequently used as a response surrogate, but remain qualitative. We hypothesized that absolute quantification of DF T2 relaxation time derived from parametric T2 maps would be a feasible and effective imaging biomarker of disease activity. Methods: This IRB-approved retrospective study included 11 patients with DF, managed by observation or systemic therapy, assessed by 3T MRI. Tumor maximum diameter, volume, and T2-weighted signal intensity were derived from manual tumor segmentations. Tumor:muscle T2 signal ratios were recorded. Two readers measured tumor T2 relaxation times using a commercial T2 scanning sequence, manual ROI delineation and commercial calculation software enabling estimation of reader reliability. Objective response rates based on RECIST1.1 and best responses were compared between sizebased and signal-based parameters. Results: Median patient age was 52.6 years; 8 subjects were female (73%). Nine patients with longitudinal assessments were followed for an average of 314 days. Median baseline tumor diameter was 7.2 cm (range 4.4 - 18.2 cm). Median baseline T2 was 65.1 ms (range 40.4 - 94.8 ms, n=11); median at last follow-up was 44.3 ms (-32% from baseline; range 29.3 - 94.7 ms, n=9). T2 relaxation times correlated with tumor:muscle T2 signal ratios, Spearman p=0.78 (p<0.001). T2 mapping showed high inter-reader reliability, ICC=0.84. The best response as a percentage change in T2 values was statistically significant (mean -17.9%, p=0.05, paired t-test) while change in diameter was not (mean -8.9%, p=0.12). Conclusions: Analysis of T2 relaxation time maps of DF may offer a feasible quantitative biomarker for assessing the extent of response to treatment. This approach may have high inter-reader reliability. [ABSTRACT FROM AUTHOR]

Anxious depression represents a subtype of major depressive disorder and is associated with increased suicidality, severity, chronicity and lower treatment response. Only a few studies have investigated the differences between anxious depressed (aMDD) and non-anxious depressed (naMDD) patients regarding treatment dosage, serum-concentration and drug-specific treatment response. In our naturalistic and prospective study, we investigated whether the effectiveness of therapy including antidepressants (SSRI, SNRI, NaSSA, tricyclics and combinations) in aMDD patients differs significantly from that in naMDD patients. In a sample of 346 patients, we calculated the anxiety somatization factor (ASF) and defined treatment response as a reduction (≥50%) in the Hamilton Depression Rating Scale (HDRS)-21 score after 7 weeks of pharmacological treatment. We did not observe an association between therapy response and the baseline ASF-scores, or differences in therapy outcomes between aMDD and naMDD patients. However, non-responders had higher ASF-scores, and at week 7 aMDD patients displayed a worse therapy outcome than naMDD patients. In subgroup analyses for different antidepressant drugs, venlafaxine-treated aMDD patients showed a significantly worse outcome at week 7. Future prospective, randomized-controlled studies should address the question of a worse therapy outcome in aMDD patients for different psychopharmaceuticals individually. [ABSTRACT FROM AUTHOR]

Hodgkin Lymphoma (HL) is a malignancy involving lymph nodes and lymphatic system. [18F]F-FDG PET/CT (FDG-PET) imaging is routinely used for staging, to assess early chemotherapy response (interim FDG-PET), at the end of treatment (EoT FDG-PET) and for the identification of disease recurrence.We present a case of a 39-year-old man treated for HL. FDG-PET scans performed after first line therapy (both Interim PET and at the end of therapy) demonstrated a persistent and significant mediastinal FDG uptake. The patient was treated with a second line therapy but the FDG-PET uptake did not change. After board discussion a new surgical, thoracoscopy-guided biopsy was performed. Histopathology demonstrated a dense fibrous tissue with occasional chronic inflammatory infiltrates.Persistent FDG-PET positivity may suggest refractory or relapsed disease. However, occasionally, non-malignant conditions are responsible for a persistent FDG uptake, not related to primary disease. An accurate evaluation of clinical history and previous imaging exams is mandatory for clinicians and others experts to avoid misinterpretations of FDG-PET results. Nevertheless, in some cases, only a more invasive procedure, such as a biopsy, may finally lead to a definitive diagnosis.

Background: Nigrostriatal microstructural integrity has been suggested as a biomarker for levodopa response in Parkinson’s disease (PD), which is a strong predictor for motor response to deep brain stimulation (DBS) of the subthalamic nucleus (STN). This study aimed to explore the impact of microstructural integrity of the substantia nigra (SN), STN, and putamen on motor response to STN-DBS using diffusion microstructure imaging. Methods: Data was collected from 23 PD patients (mean age 63 ± 7, 6 females) who underwent STN-DBS, had preoperative 3 T diffusion magnetic resonance imaging including multishell diffusion-weighted MRI with b-values of 1000 and 2000 s/mm2 and records of motor improvement available. Results: The association between a poorer DBS-response and increased free interstitial fluid showed notable effect sizes (rho > |0.4|) in SN and STN, but not in putamen. However, this did not reach significance after Bonferroni correction and controlling for sex and age. Conclusion: Microstructural integrity of SN and STN are potential biomarkers for the prediction of therapy efficacy following STN-DBS, but further studies are required to confirm these associations.

Background and aimsRheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).MethodsDNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.ResultsOf the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.ConclusionOur findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.

ObjectivesBecause size-based imaging criteria poorly capture biologic response in desmoid-type fibromatosis (DF), changes in MRI T2 signal intensity are frequently used as a response surrogate, but remain qualitative. We hypothesized that absolute quantification of DF T2 relaxation time derived from parametric T2 maps would be a feasible and effective imaging biomarker of disease activity.MethodsThis IRB-approved retrospective study included 11 patients with DF, managed by observation or systemic therapy, assessed by 3T MRI. Tumor maximum diameter, volume, and T2-weighted signal intensity were derived from manual tumor segmentations. Tumor:muscle T2 signal ratios were recorded. Two readers measured tumor T2 relaxation times using a commercial T2 scanning sequence, manual ROI delineation and commercial calculation software enabling estimation of reader reliability. Objective response rates based on RECIST1.1 and best responses were compared between size-based and signal-based parameters.ResultsMedian patient age was 52.6 years; 8 subjects were female (73%). Nine patients with longitudinal assessments were followed for an average of 314 days. Median baseline tumor diameter was 7.2 cm (range 4.4 - 18.2 cm). Median baseline T2 was 65.1 ms (range 40.4 - 94.8 ms, n=11); median at last follow-up was 44.3 ms (-32% from baseline; range 29.3 - 94.7 ms, n=9). T2 relaxation times correlated with tumor:muscle T2 signal ratios, Spearman p=0.78 (p

Ferroptosis is a novel kind of iron‐ and reactive oxygen‐induced cell death, investigation into ferroptosis‐associated long noncoding RNAs (FALs) in clear cell renal cell carcinoma (ccRCC) is scarce. The goal of the research was to look at FALs' possible predictive significance, as well as their interaction with the immune microenvironment and therapeutic responsiveness of ccRCC. The Cancer Genome Atlas database was employed to retrieve RNA sequencing data from 530 individuals with ccRCC. Patients with ccRCC were randomly assigned to one of two groups: training or testing. Pearson's correlation analysis through the identified ferroptosis‐related genes was implemented to screen for FALs. Finally, a FALs signature composed of eight lncRNAs was discovered for predicting survival outcomes in ccRCC patients. ccRCC patients in the training, testing, and overall cohorts were separated into low‐risk and high‐risk groups based on their risk score. The FALs signature was identified to be an independent factor for overall survival in the multivariate Cox analysis (hazard ratio = 1.013, 95% confidence interval = 1.008–1.018, p < 0.001). A clinically prognostic nomogram was created depending on the FALs signature and clinical characteristics. The nomogram provides greater clinical practicability and may reliably estimate patients' overall survival. The FALs signature may additionally precisely represent ccRCC's immunological environment, immunotherapy reaction, and drug sensitivity. The eight FALs and their signature provide precise and reliable methods for evaluating the clinical effects of in ccRCC patients, and they could be biological markers and targets for therapy. [ABSTRACT FROM AUTHOR]

Introduction: The availability of highly effective triple cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination therapy with elexacaftor--tezacaftor--ivacaftor (ETI) has improved pulmonary outcomes and quality of life of people with cystic fibrosis (pwCF). The aim of this study was to assess computed tomography (CT) changes under ETI visually with the Brody score and quantitatively with dedicated software, and to correlate CT measures with parameters of clinical response. Methods: Twenty two adult pwCF with two consecutive CT scans before and after ETI treatment initiation were retrospectively included. CT was assessed visually employing the Brody score and quantitatively by YACTA, a well-evaluated scientific software computing airway dimensions and lung parenchyma with wall percentage (WP), wall thickness (WT), lumen area (LA), bronchiectasis index (BI), lung volume and mean lung density (MLD) as parameters. Changes in CT metrics were evaluated and the visual and quantitative parameters were correlated with each other and with clinical changes in sweat chloride concentration, spirometry [percent predicted of forced expiratory volume in one second (ppFEV1)] and body mass index (BMI). Results: The mean (SD) Brody score improved with ETI [55 (12) vs. 38 (15); p < 0.001], incl. sub-scores for mucus plugging, peribronchial thickening, and parenchymal changes (all p < 0.001), but not for bronchiectasis (p = 0.281). Quantitatve WP (p < 0.001) and WT (p = 0.004) were reduced, conversely LA increased (p = 0.003), and BI improved (p = 0.012). Lung volume increased (p < 0.001), and MLD decreased (p < 0.001) through a reduction of ground glass opacity areas (p < 0.001). Changes of the Brody score correlated with those of quantitative parameters, exemplarily WT with the sub-score for mucus plugging (r = 0.730, p < 0.001) and peribronchial thickening (r = 0.552, p = 0.008). Changes of CT parameters correlated with those of clinical response parameters, in particular ppFEV1 with the Brody score (r = -0.606, p = 0.003) and with WT (r = -0.538, p = 0.010). Discussion: Morphological treatment response to ETI can be assessed using the Brody score as well as quantitative CT parameters. Changes in CT correlated with clinical improvements. The quantitative analysis with YACTA proved to be an objective, reproducible and simple method for monitoring lung disease, particularly with regard to future interventional clinical trials. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer in all its forms of expression is a major cause of death. The bladder cancer is also causes the same. finding the biomarkers responsible for the cancer is a challenging task and in certain cases, such as disease progression and therapy response, it become more challenging. The advancements in technology provides latest machine learning methods that help to identify the genomic biomarkers computationally. In this paper, the genomic biomarkers are tracked for bladder cancer from Univeristy of Calgary cohort and different bioinformatics methods, such as differential gene expression, survival rate estimation, consensus gene selection approaches were optimally used. The elastic-net based regression method has been utilized as a machine learning method which shows satisfactory results. Cancer in all its forms of expression is a major cause of death. To identify the genomic reason behind cancer, discovery of biomarkers is needed. In this paper, genomic data of bladder cancer are examined for the purpose of biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either at a very low level based on the genome sequence itself, or more abstractly such as measuring the level of gene expression for different disease groups. The latter method is pivotal for this work, since the available datasets consist of RNA sequencing data, transformed to gene expression levels, as well as data on a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan–Meier curves, and heatmaps for the experiments leading to biomarker discovery. The experiments have led to the discovery of two gene signatures capable of predicting therapy response and disease progression with considerable accuracy for bladder cancer patients which correlates well with clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is the most common type of cancer worldwide. Cyclin-dependent kinase inhibition is one of the backbones of metastatic breast cancer therapy. However, there are a significant number of therapy failures. This study evaluates the biomarker potential of microRNAs for the prediction of a therapy response under cyclin-dependent kinase inhibition. Methods: This study comprises the analysis of intracellular and extracellular microRNA-expression-level alterations of 56 microRNAs under palbociclib mono as well as combination therapy with letrozole. Breast cancer cell lines BT-474, MCF-7 and HS-578T were analyzed using qPCR. Results: A palbociclib-induced microRNA signature could be detected intracellularly as well as extracellularly. Intracellular miR-10a, miR-15b, miR-21, miR-23a and miR-23c were constantly regulated in all three cell lines, whereas let-7b, let-7d, miR-15a, miR-17, miR-18a, miR-20a, miR-191 and miR301a_3p were regulated only in hormone-receptor-positive cells. Extracellular miR-100, miR-10b and miR-182 were constantly regulated across all cell lines, whereas miR-17 was regulated only in hormone-receptor-positive cells. Conclusions: Because they are secreted and significantly upregulated in the microenvironment of tumor cells, miRs-100, -10b and -182 are promising circulating biomarkers that can be used to predict or detect therapy responses under CDK inhibition. MiR-10a, miR-15b, miR-21, miR-23a and miR-23c are potential tissue-based biomarkers. [ABSTRACT FROM AUTHOR]

The polymorphic microbiome has been proposed as a new hallmark of cancer. Intratumor microbiome has been revealed to play vital roles in regulating tumor initiation and progression, but the regulatory mechanisms have not been fully uncovered. In this review, we illustrated that similar to other components in the tumor microenvironment, the reside and composition of intratumor microbiome are regulated by tumor cells and the surrounding microenvironment. The intratumor hypoxic, immune suppressive, and highly permeable microenvironment may select certain microbiomes, and tumor cells may directly interact with microbiome via molecular binding or secretions. Conversely, the intratumor microbiomes plays vital roles in regulating tumor initiation and progression via regulating the mutational landscape, the function of genes in tumor cells and modulating the tumor microenvironment, including immunity, inflammation, angiogenesis, stem cell niche, etc. Moreover, intratumor microbiome is regulated by anti‐cancer therapies and actively influences therapy response, which could be a therapeutic target or engineered to be a therapy weapon in the clinic. This review highlights the intratumor microbiome as a vital component in the tumor microenvironment, uncovers potential mutual regulatory mechanisms between the tumor microenvironment and intratumor microbiome, and points out the ongoing research directions and drawbacks of the research area, which should broaden our view of microbiome and enlighten further investigation directions. [ABSTRACT FROM AUTHOR]