Differences In Treatment Response Research Articles

Differential Vulnerability and Response to Injury Among Brain Cell Types Comprising the Neurovascular Unit.

The neurovascular unit includes multiple different cell types, including neurons, astrocytes, endothelial cells and pericytes, which respond to insults on very different time or dose scales. We defined differential vulnerability among these cell types, using response to two different insults: oxygen-glucose deprivation and thrombin-mediated cytotoxicity. We found that neurons are most vulnerable, followed by endothelial cells, followed by astrocytes. After temporary focal cerebral ischemia in male rats, we found significantly more injured neurons, compared to astrocytes in the ischemic area, consistent with differential vulnerability in vivo. We sought to illustrate different and shared mechanisms across all cell types during response to insult. We found that gene expression profiles in response to oxygen-glucose deprivation differed among the cell types, with a paucity of gene responses shared by all types. All cell types activated genes relating to autophagy, apoptosis, and necroptosis, but the specific genes differed. Astrocytes and endothelial cells also activated pathways connected to DNA repair and anti-apoptosis. Taken together, the data support the concept of differential vulnerability in the neurovascular unit and suggests that different elements of the unit will evolve from salvageable to irretrievable on different time scales while residing in the same brain region and receiving the same (ischemic) blood flow. Future work will focus on the mechanisms of these differences. These data suggest future stroke therapy development should target different elements of the NVU differently.Significance Statement For decades, stroke treatments proven effective in pre-clinical models have failed in human clinical trials. Experimental, pre-clinical evaluation has focused mostly on protecting neurons, assuming that glia and vascular cells also would be protected. We now define and demonstrate differential vulnerability to insult as well as differential response to treatment among the various brain cell types comprising the neurovascular unit. Neurons, astrocytes, and endothelial cells show markedly different responses to ischemia. Ignoring differential vulnerability and treatment response may explain past clinical trial failures. Future studies should determine the mechanisms that underly differential vulnerability in the neurovascular unit.

GSDMB/ORMDL3 Rare/Common Variants Are Associated with Inhaled Corticosteroid Response among Children with Asthma

Inhaled corticosteroids (ICS) are efficacious in the treatment of asthma, which affects more than 300 million people in the world. While genome-wide association studies have identified genes involved in differential treatment responses to ICS in asthma, few studies have evaluated the effects of combined rare and common variants on ICS response among children with asthma. Among children with asthma treated with ICS with whole exome sequencing (WES) data in the PrecisionLink Biobank (91 White and 20 Black children), we examined the effect and contribution of rare and common variants with hospitalizations or emergency department visits. For 12 regions previously associated with asthma and ICS response (DPP10, FBXL7, NDFIP1, TBXT, GLCCI1, HDAC9, TBXAS1, STAT6, GSDMB/ORMDL3, CRHR1, GNGT2, FCER2), we used the combined sum test for the sequence kernel association test (SKAT) adjusting for age, sex, and BMI and stratified by race. Validation was conducted in the Biorepository and Integrative Genomics (BIG) Initiative (83 White and 134 Black children). Using a Bonferroni threshold for the 12 regions tested (i.e., 0.05/12 = 0.004), GSDMB/ORMDL3 was significantly associated with ICS response for the combined effect of rare and common variants (p-value = 0.003) among White children in the PrecisionLink Biobank and replicated in the BIG Initiative (p-value = 0.02). Using WES data, the combined effect of rare and common variants for GSDMB/ORMDL3 was associated with ICS response among asthmatic children in the PrecisionLink Biobank and replicated in the BIG Initiative. This proof-of-concept study demonstrates the power of biobanks of pediatric real-life populations in asthma genomic investigations.

Abstract 6403: Presence of Stroma AReactive Invasion Front Areas (SARIFA) - a potential novel histopathological tool for therapy response prediction in colorectal cancer

Abstract Background: Although many approaches based either on molecular investigations or on deep-learning algorithms applied to tissue sections have been proposed to stratify colorectal cancers (CRC) patients, clinical implementation of these novel cost-and time-consuming techniques is lacking, and conventional tumor-node-metastasis (TNM) staging remains one of the key factors to determine patient’s treatment. Hence, we developed a novel and solely Haematoxylin and Eosin (H&E) based histopathologic prognostic biomarker, called SARIFA (Stroma AReactive Invasion Front Areas, i.e., direct contact between tumor cells and fat cells). Materials/Methods: Based on a previous study, in which we could show that gene-expression based computational drug response prediction (oncoPredict) indicates a SARIFA-dependent differential drug sensitivity, we investigated whether SARIFA-status can predict which CRC patients might benefit from adjuvant therapy. SARIFA-status was established for 1,727 CRC patients from the prospective Netherlands Cohort Study. Multivariate-adjusted Cox regression analysis was performed (median follow-up: 4.79 years, with 1,463 deaths being observed in the first 10 years of follow-up [933, 63.8%, were CRC-related]). Only stages II to IV were included for further analysis regarding differential treatment response (n = 1,384). Results: In Multivariate-adjusted Cox regression analysis including patient age, sex, tumor location, pTNM stage, grade of differentiation, and mismatch repair status, SARIFA-positive CRC patients (n=496) benefitted significantly from adjuvant therapy (HRCRC-specific 0.59; 95% CI 0.44-0.78) compared to SARIFA-positive patients treated with surgery alone, which was not significant for SARIFA-negative CRC patients (HRCRC-specific 0.88; 95% CI 0.68-1.15). Regarding overall survival (OS), adjuvant therapy led to better OS outcomes regardless of SARIFA-status (HRoverall all patients 0.74; 95% CI 0.62-0.87). Conclusion: Our results suggest that SARIFA-positivity predicts a CRC-specific survival benefit from adjuvant therapy. As SARIFA-positivity is closely linked to an upregulation of lipid metabolism and an altered immune response, further studies are warranted to explore the potential benefit of not only conventional chemotherapy but also immunotherapy, targeted therapy as well novel drugs targeting lipid-metabolism. As SARIFA-status can be assessed fast and easily on for every cancer patient available H&E slides, SARIFA-status could serve as an ideal biomarker for a more detailed patient stratification in prospective clinical trials, and should be implemented into clinical routine if further validated. Citation Format: Nic G. Reitsam, Kelly Offermanns, Bianca Grosser, Jessica Zimmermann, Colinda Simons, Jakob N. Kather, Piet A. van den Brandt, Bruno Märkl, Heike I. Grabsch. Presence of Stroma AReactive Invasion Front Areas (SARIFA) - a potential novel histopathological tool for therapy response prediction in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6403.

Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes

RationaleHeterogeneity of the host response within sepsis, acute respiratory distress syndrome (ARDS) and more widely critical illness, limits discovery and targeting of immunomodulatory therapies. Clustering approaches using clinical and circulating...

Abstract A069: High-resolution volumetric imaging of primary and metastatic high-grade serous ovarian cancer explants

Abstract Introduction: As high-grade serous ovarian cancer (HGSOC) rarely shows symptoms, most patients present with metastases. Tumor heterogeneity between metastases may impede a patient’s response to therapy and thus impact survival. We aim to decipher the heterobiology of HGSOC lesions using high-resolution imaging of patient-derived explants (PDEs), to determine the differential treatment response between primary and metastatic tumors. Methods: PAX8, CK7, RAD51, γH2AX, Ki67, cPARP, and CD3 were chosen to enumerate tumor and T cells, and their activity. Antibodies were optimized in monolayer and spheroid cultures of HGSOC cell lines, COV318 and OVCAR4. CD3 was optimized in FFPE human tonsillitis tissue. Primary and metastatic HGSOC explants were cultured for 48h followed by 24h fixation. Several immunostaining and optical clearing protocols were tested on PDEs, which were imaged using a fully automated confocal high-content screening system. Results: Immunostaining and automated image analysis facilitated the identification of individual nuclei within PDEs without Refractive Index (RI) matching or optical clearing. However, high background impeded further image analysis, preventing the quantification of certain antibodies. Glycerol-based RI matching and ScaleS tissue clearing were implemented to try and overcome this. However, neither generated sufficiently high-quality images for analysis. A novel Clearing-enhanced 3D (Ce3D) immunostaining system recently developed for high-quality multiplex fluorescence microscopy of large tissue volumes was tested. While individual nuclei were recognized, as well as RAD51 foci and CK7 cytoskeleton staining, image analysis was still impeded due to high background. Conclusions: Protocols without RI matching and optical clearing can stain large, complex structures such as PDEs. However, high background impedes image analysis suggesting the need for optical clearing and RI matching. The inclusion of various RI matching and/or optical clearing steps failed to overcome high background staining. Further work is ongoing to refine immunostaining, RI matching, optical clearing, and mounting of PDEs to allow the quantification of all antibodies. Citation Format: Adele E. Connor, Joanne Lysaght, Donal J. Brennan, Jeremy C. Simpson, Antoinette S. Perry. High-resolution volumetric imaging of primary and metastatic high-grade serous ovarian cancer explants [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A069.

Evaluating the heterogeneity of hippocampal avoidant whole brain radiotherapy treatment effect: A secondary analysis of NRG CC001

Abstract Background Hippocampal avoidant whole brain radiotherapy (HA-WBRT) is the standard of care for patients needing WBRT for brain metastases. This study, using existing data from NRG Oncology CC001 including baseline tumor characteristics and patient-reported MD Anderson Symptom Inventory-Brain Tumor (MDASI-BT) scores, sought to identify subgroups of patients that demonstrate differential neuroprotective treatment response to HA-WBRT. Methods An exploratory analysis of NRG CC001, a phase 3 trial in which 518 patients were randomly assigned to WBRT plus memantine or HA-WBRT plus memantine, was performed. Rates of neurocognitive function failure (NCFF) were estimated between subgroups and stratified by arm. Covariate and subgroup interaction with differential treatment response were calculated. Results The benefit of HA-WBRT on decreasing NCFF was seen in patients living ≥ 4 months (HR 0.75, 95% CI: 0.58–0.97, P = .03), whereas patients living < 4 months derived no significant neurocognitive benefit. A significant association between baseline MDASI-BT cognitive factor and treatment response (interaction P = .03) was identified. Patients with lower MDASI-BT scores (less patient-reported cognitive impairment) derived significantly greater benefit (HR = 0.64, 95% CI: 0.48–0.85, P = .002) compared to those with highest MDASI-BT scores (HR = 1.24, 95% CI: 0.76–2.04, P = .39). Tumor histology also had a significant interaction (P = .01) with treatment response. Primary lung histology patients derived cognitive failure risk reduction (HR = 0.58, 95% CI: 0.43–0.77, P = .0007) from HA-WBRT, in contrast to nonlung primary histology patients (HR = 1.15, 95% CI: 0.78–1.50, P = .48). Conclusions Differential neuroprotective response to HA-WBRT was identified in this analysis. Patients surviving ≥ 4 months derived benefit from HA-WBRT. There is evidence of heterogeneity of treatment effect for patients with less severe patient-reported cognitive impairment at baseline and those with primary lung histology.

Comparative Analysis of Methods for Analyzing Clinical Results of Immuno-Oncology Therapy with Delayed Treatment Effects

Introduction: CAR-T therapy is a groundbreaking therapy that involves modifying patient's T cells to recognize and combat cancer cells. This therapy requires a period of CAR-T manufacture following apheresis, delaying the actual receipt of the CAR-T. During this period the patient may receive bridging therapy. CAR-T therapies are increasingly being studied in randomized controlled clinical trials and delays potentially affect the proportional hazards assumption, reduce log-rank test statistical power, and complicate reporting and interpretation. To address this, we assessed available statistical methods for analyzing immune-oncology outcomes, specifically CAR-T therapy's delayed treatment effects, with a focus on current and emerging tools to improve analysis and reporting. Methods: We systematically reviewed and compared existing and emerging tools to evaluate survival analysis methods suitable for assessing outcomes in CAR-T studies. These methods included standard, stratified, and weighted log-rank tests, cox proportional hazards (time dependent), parametric survival (weibull, etc.), time-dependent covariates, landmark analysis, joint modeling and restricted mean survival time (RMST). We simulated trial results, highlighted features, underlying assumptions, and interpretability. Results: Results were based on a simulated dataset (N=240) and revealed distinct characteristics and implications of each method used to analyze data (Table). Standard log-rank tests, which give equal weight to all time points and assumes proportional hazards, resulted in less discerning outcome measurements compared to weighted techniques such as censored weighted estimation/constant piecewise weighted (CWE/CPW) and Fleming-Harrington which do not assume proportional hazards and can be weighted towards early or late events. RMST summarized average time until progression, which may be a more intuitive measure for clinicians and patients and was presented along with other analytical techniques. Cox proportional hazards modeling provided ratios to quantify and compare efficacy across different time points. Parametric survival models captured survival dynamics and offered precise estimation of survival probabilities at specific intervals. Time-dependent covariates accounted for time-varying factors influencing treatment response, while landmark analysis evaluated treatment efficacy beyond a specific duration and was used in combination with other analytical techniques. Joint modeling simultaneously assessed treatment effects on both longitudinal and survival outcomes, accounting for their interdependencies. Stratification identified patient subgroups with differential treatment responses and outcomes, enabling precise reporting of categorical CAR-T treatment approaches. Conclusions: Our comparative analysis of analytical techniques underscores the critical role of selecting an appropriate approach in the context of CAR-T therapies. Conventional methods such cox regression and the standard log-rank test, while useful in most contexts, may underestimate key outcomes associated with CAR-T therapy due to violations of proportional hazard assumption. Weighted techniques, which can be weighted towards early or late events, may provide a more nuanced picture, and better accommodate CAR-T therapies. Similarly, the RMST offers an intuitive average time to progression, offering complementary insights to other methods. These findings highlight the importance of careful method selection in survival analysis for CAR-T therapy studies. Rigorous, tailored analytical approaches will be crucial in accurately determining the efficacy of these promising therapies and informing future clinical study designs. By considering these methods, researchers can gain deeper insights regarding CAR-T therapy and improve the design and analysis of clinical trials, ultimately advancing the understanding and application of this transformative therapy.

CD5 Expression in Mature T Cell Lymphoma

Background Mature T-cell lymphoma (TCL), an aggressive and heterogenous group of T cell neoplasms, constitutes less than 15% of adult non-Hodgkin lymphomas. CD5 is an inhibitory T cell receptor molecule expressed on mature T cells. CD5 expression has been associated with worse outcomes in B-cell malignancies, however this association has not been explored in TCL. Furthermore, CD5 has become a promising immunotherapeutic target for TCL. We herein aimed to characterize CD5 expression in TCL subtypes at diagnosis and relapse, and its association with clinical features and outcomes in a single center cohort. Methods We retrospectively identified 109 patients (pts) diagnosed with non-cutaneous and non-leukemic mature TCL, with data on CD5 expression on tumor tissue by immunohistochemistry (IHC) and/or flow cytometry (FC), from the electronic medical records at the Hospital of the University of Pennsylvania (HUP). CD5 positivity (CD5+) was defined as any percentage of CD5+ malignant cells on FC and/or IHC. Chi-square test was used to compare baseline pt characteristics at the initial presentation between CD5 expression groups. Treatment response (TR) was defined as achieving complete or partial response by the end of initial treatment. Logistic regression models were used to assess the impact of clinical parameters on the overall TR rate and odds ratios (OR) were computed. Cox proportional hazards models were used to evaluate the effects of clinical parameters on overall survival (OS) and progression-free survival (PFS) and were reported as hazard ratios (HR). We also conducted Kaplan-Meier (KM) analyses for OS and PFS by CD5 expression status. The OR and HR are reported with 95% CI in brackets. P-values <0.05 were considered significant. All analyses were conducted in SAS version 9.4 (SAS Institute Inc). Results We collected and evaluated data from a total of 109 adult pts diagnosed with TCL at HUP between 1/1/05-1/1/21. Median age at diagnosis was 58 (range: 19 - 86 years); 56% were men; the most common diagnosis was ALCL (n=37) followed by nodal TFH lymphoma (n=29), PTCL-NOS (n=16), ENKTCL (n=13) and other extranodal (EN) TCL (n=14) (Table.1). Compared to CD5- pts, a higher percentage of CD5+ pts were diagnosed at age >60 (CD5-:34.7%, CD5+:55.0%, p=0.0343), and stage 3 or 4 (CD5-:46.9%, CD5+:70%, p=0.0142), but fewer CD5+ pts had EN involvement at diagnosis (CD5-:77.6%, CD5+:50%, p=0.0031). CD5 was also differentially expressed by TCL subtypes: nearly all TFH TCL (93.1%) and PTCL-NOS expressed CD5 (87.5%), whereas CD5 was less commonly expressed in ALCL (39.4%,), other ENTCL (27.8%), and ENKTCL (7.7%) (p<0.0001). In addition, on univariate analysis (UVA), CD5 expression was associated with stage 3 or 4 (p=0.014), age >60 (p=0.034), and less common EN involvement (p=0.031), but not with treatment response, relapse or survival. On UVA for treatment response, established risk factors such as ≥ 2 EN involvement (OR=0.25 [0.09, 0.6], p=0.0243) and high LDH (OR=0.26 [0.08, 0.85], p=0.0267), were shown to be negative predictors. Multivariate analysis (MVA) for response also predicted high LDH as a negative predictor (OR=0.29 [0.08,0.99], p=0.0477). CD5 expression was not associated with treatment response on either UVA (OR=1.18 [0.50, 2.78], p=0.7042) or MVA (OR=1.00 [0.33,3.60], p=0.9969). On UVA, CD5 positivity did not impact OS (HR=1.2 [0.7,2.0], p=0.6118) nor PFS (HR=1.5 [0.7,3.0], p=0.2862), while worse outcomes were associated with established risk factors, including ECOG performance status ≥ 2 (OS HR=2.0 [1.0-3.8], p=0.0440), ≥2 EN involvement (OS HR=3.1 [1.7-5.7], p=0.0002; PFS HR 13.1 [2.9-59.9], p=0.0009), and B symptoms (OS HR=2.1 [1.2-3.7], p=0.0117). TFH TCL was also found to be associated with worse OS (HR= 2.6 [1.3-5.5], p=0.0101). In our cohort of 35 pts with CD5 expression data at relapses, 100% pts remained CD5+ (n=19), 68.75% pts remained CD5- (n=11), but 31.25% pts gained CD5 expression (n=5), and none lost CD5 expression. Based on KM analyses, CD5 expression did not impact PFS or OS (Fig. 1). Conclusions In this cohort of 109 TCL pts, CD5 expression was common in TFH TCL, PTCL-NOS, ALCL, and ENTCL, and was retained or gained at relapse, thus supporting its potential as a therapeutic target. CD5 expression was associated with advanced age and stage, while EN involvement was less common. CD5 expression was not associated with a differential treatment response, PFS, nor OS.

Novel biomarker SARIFA in colorectal cancer: highly prognostic, not genetically driven and histologic indicator of a distinct tumor biology

SARIFA (Stroma AReactive Invasion Front Areas) has recently emerged as a promising histopathological biomarker for colon and gastric cancer. To elucidate the underlying tumor biology, we assessed SARIFA-status in tissue specimens from The-Cancer-Genome-Atlas (TCGA) cohorts COAD (colonic adenocarcinoma) and READ (rectal adenocarcinoma). For the final analysis, 207 CRC patients could be included, consisting of 69 SARIFA-positive and 138 SARIFA-negative cases. In this external validation cohort, H&E-based SARIFA-positivity was strongly correlated with unfavorable overall, disease-specific, and progression-free survival, partly outperforming conventional prognostic factors. SARIFA-positivity was not associated with known high-risk genetic profiles, such as BRAF V600E mutations or microsatellite-stable status. Transcriptionally, SARIFA-positive CRCs exhibited an overlap with CRC consensus molecular subtypes CMS1 and CMS4, along with distinct differential gene expression patterns, linked to lipid metabolism and increased stromal cell infiltration scores (SIIS). Gene-expression-based drug sensitivity prediction revealed a differential treatment response in SARIFA-positive CRCs. In conclusion, SARIFA represents the H&E-based counterpart of an aggressive tumor biology, demonstrating a partial overlap with CMS1/4 and also adding a further biological layer related to lipid metabolism. Our findings underscore SARIFA-status as an ideal biomarker for refined patient stratification and novel drug developments, particularly given its cost-effective assessment based on routinely available H&E slides.

The development and psychometric evaluation of the Questionnaire Epistemic Trust (QET): A self‐report assessment of epistemic trust

AbstractEpistemic trust (ET) refers to the predisposition to trust information as authentic, trustworthy and relevant to the self. Epistemic distrust – resulting from early adversity – may interfere with openness to social learning within the therapeutic encounter, reducing the ability to benefit from treatment. The self‐report Questionnaire Epistemic Trust (QET) is a newly developed instrument that aims to assess ET. This study presents the first results on the psychometric properties of the QET in both a community and a clinical sample. Our findings indicate that the QET is composed of four meaningful subscales with good to excellent internal consistency. The QET shows relevant associations with related constructs like personality functioning, symptom distress and quality of life. QET scores clearly distinguish between a clinical and community sample and are associated with the quality of the therapeutic alliance. The QET provides a promising, brief and user‐friendly instrument that could be used for a range of clinical and research purposes. Future studies with larger samples are needed to strengthen construct validity and to investigate the value of the QET to predict differential treatment responses or to study mechanisms of change.

CBT-I treatment attrition in patients with weekly nightmares.

This study's objective was to evaluate the effect of nightmares (NM) on attrition and symptom change following CBT-I treatment using data from a successful CBT-I randomized controlled trial (RCT) delivered to participants with recent interpersonal violence exposure. The study randomized 110 participants (107 women, mean age 35.5), to CBT-I or an attention control group. Participants were assessed at three time periods: Baseline, post-CBT-I (or attention control), and at T3 post-Cognitive Processing Therapy, received by all participants. NM reports were extracted from the Fear of Sleep Inventory. Participants with weekly NM were compared to those with fewer than weekly nightmares on outcomes including attrition, insomnia, PTSD, and depression. Change in NM frequency was examined. Participants with weekly NM (55%) were significantly more likely to be lost to follow up (LTF) post-CBT-I (37%), compared to 15.6% of participants with infrequent NM and were less likely to complete T3 (43%) than patients with less frequent NM (62.5%). NM were unrelated to differential treatment response in insomnia, depression or PTSD. Treatment with CBT-I was not associated with reduced NM frequency; however, change in sleep onset latency (SOL) from post-CBT-I to T3 predicted fewer nightmares at T3. Weekly NM were associated with attrition, but not a reduced change in insomnia symptoms following CBT-I. NM symptoms did not change as a function of CBT-I but change in SOL predicted lower NM frequency. CBT-I trials should screen for NM and consider augmenting CBT-I to specifically address NMs.

Metastatic Organotropism Differential Treatment Response in Urothelial Carcinoma: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials

Metastatic Organotropism Differential Treatment Response in Urothelial Carcinoma: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials

Impact of different CRPS phenotypes and diagnostic criteria on quantitative sensory testing outcomes: systematic review and meta-analysis.

This review and meta-analysis evaluated the impact of diagnostic criteria and clinical phenotypes on quantitative sensory testing (QST) outcomes in patients with complex regional pain syndrome (CRPS). Eight databases were searched based on a previously published protocol. Forty studies comparing QST outcomes between CRPS-I vs II, warm vs cold CRPS, upper vs lower limb CRPS, males vs females, or using Budapest vs older IASP criteria were included. Studies investigating QST differences between CRPS-I vs II (n = 4), between males vs females (n = 2), and between upper and lower limb CRPS (n = 2) showed no significant differences. Four studies compared QST outcomes in warm vs cold CRPS, showing heat hyperalgesia in warm CRPS, with thermal and mechanical sensory loss in cold CRPS. Although CRPS diagnosed using the Budapest criteria (24 studies) vs 1994 IASP criteria (13 studies) showed similar sensory profiles, there was significant heterogeneity and low quality of evidence in the latter. Based on the findings of this review, classifying CRPS according to presence or absence of nerve lesion into CRPS-I and II, location (upper or lower limb) or according to sex might not be clinically relevant as all appear to have comparable sensory profiles that might suggest similar underlying mechanisms. In contrast, warm vs cold phenotypes exhibited clear differences in their associated QST sensory profiles. To the extent that differences in underlying mechanisms might lead to differential treatment responsiveness, it appears unlikely that CRPS-I vs II, CRPS location, or patient sex would prove useful in guiding clinical management.

Decoding TROP2 in breast cancer: significance, clinical implications, and therapeutic advancements.

Breast cancer is a heterogeneous disease characterized by distinct molecular subtypes, varied prognoses, and differential treatment responses. Understanding the molecular landscape and identifying therapeutic targets, such as trophoblast cell-surface antigen 2 (TROP2), is vital. TROP2 is notably overexpressed in breast cancer, playing a significant role in tumor growth, invasion, metastasis, and treatment resistance. While significant progress has been made in targeting TROP2 in breast cancer, several challenges and knowledge gaps remain. These challenges include the heterogeneity of TROP2 expression within breast cancer subtypes, resistance to its targeted therapies, potential off-target effects, limited therapeutic agents, and identifying optimal combination treatments. Integrating findings from clinical trials into clinical practice further complicates the landscape. This review article delves deep into TROP2 in breast cancer, highlighting its expression patterns, clinical implications, and therapeutic advancements. By understanding the role of TROP2, we can pave the way for personalized treatments, and transform the landscape of breast cancer care.

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies

BackgroundMajor depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results. In order to improve the interpretability of ML models in clinical practice, our goal was to demonstrate the derivation of treatment-relevant patient profiles comprised of clinical and demographic information using a novel ML approach. MethodsWe analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a ML model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes. ResultsA 3-prototype model achieved an area under the receiver operating curve of 0.66 and an expected absolute improvement in remission rate for those receiving the best predicted treatment of 6.5% (relative improvement of 15.6%) compared to the population remission rate. We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue, and more severe symptoms. Cluster B patients tended to be older, female, have less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, and more somatic genital symptoms. ConclusionIt is possible to produce novel treatment-relevant patient profiles using ML models; doing so may improve interpretability of ML models and the quality of precision medicine treatments for MDD.

Impact of dissociation on exposure therapy for PTSD outcomes and Adherence among U.S. Military service members

Emotional engagement is necessary for successful exposure therapy for posttraumatic stress disorder (PTSD), but dissociation is considered a barrier to emotional engagement. Virtual reality exposure therapy (VRE) uses multi-sensory virtual environments to increase emotional engagement during exposure therapy, and average treatment outcomes are comparable to traditional exposure therapy. However, individual factors (e.g., depression) can predict differential responses to VRE. Studies have yet to investigate whether VRE would be more effective in treating patients with dissociation compared to traditional PE. This secondary analysis of a randomized clinical trial explores whether dissociation predicts treatment outcomes to exposure therapy among active-duty soldiers (N = 108) diagnosed with PTSD. We also examine whether individuals reporting dissociative symptoms demonstrated differential treatment responses to VRE and PE. Results indicated a significant two-way interaction between dissociation and time in treatment, such that dissociation blunted the negative relationship between time and PTSD symptoms. Dissociation was not associated with treatment session attendance or drop out. Results also revealed no significant effect of treatment group (PE or VRE) on the relationship between dissociation and PTSD symptoms. Findings contribute to a body of literature supporting the potential clinical and research utility of a dissociative subtype of PTSD.

P15.04.A BIOLOGICAL CORRELATES OF LEAKAGE EFFECT METRICS COMPUTED FROM DYNAMIC SAGE-EPI MRI IN ENHANCING AND NON-ENHANCING HUMAN GLIOMAS

Abstract BACKGROUND Dynamic spin-and-gradient-echo echoplanar imaging (SAGE-EPI) can be used to compute novel quantitative maps derived from contrast leakage effects, which are thought to reflect tissue characteristics, while acquiring perfusion imaging. In this study, we explored leakage effect metrics and their biological correlates in human gliomas. METHODS 57 patients with histology-proven gliomas were studied (38 enhancing, 19 non-enhancing). The following quantitative leakage effect maps were computed: ΔR2,ss* (reflecting T2*-leakage effects, dependent on cytoarchitecture), ΔR1,ss (reflecting T1-leakage effects, dependent on blood-brain barrier permeability), and the transverse relaxivity at tracer equilibrium (TRATE, reflecting the balance between ΔR2,ss* and ΔR1,ss). In enhancing gliomas, these metrics were compared between patient subgroups (treatment-naïve [TN] vs recurrent [R]) and biological features (IDH status, Ki67 expression). In non-enhancing gliomas, qualitative and quantitative analyses were performed to evaluate the potential usefulness of leakage effects in the absence of gross contrast enhancement. RESULTS In enhancing IDH-wild-type gliomas (IDHwt), previous exposure to treatment determined higher ΔR1,ss (p=0.035) and lower TRATE (p=0.002). In IDH-mutant-gliomas (IDHm), previous treatment determined higher ΔR1,ss (p=0.026). In TN-gliomas, IDHwt status tended to determine higher ΔR1,ss and lower TRATE (p=0.13). ΔR1,ss values were correlated with Ktrans values (TN/R p=0.036/0.0024, r=0.68/0.59). TRATE values above 142 mM-1s-1 were exclusively seen in TN-IDHwt, and in TN-gliomas this cutoff had 89% sensitivity and 80% specificity as a predictor of Ki67>10%. Preliminary analyses on non-enhancing gliomas revealed that a subset of gliomas (n=11) showing a visual tumor-to- BACKGROUND contrast in leakage effect maps were higher in grade (p=0.049), and all grade 4 gliomas (n=2) displayed pronounced leakage effects. CONCLUSION s: In enhancing gliomas novel leakage effect metrics computed with dynamic SAGE-EPI may reflect cytoarchitecture and blood-brain barrier disruption. More in detail, TRATE was higher in IDHwt-gliomas and high-Ki67 gliomas, consistently with the hypothesis of TRATE values being proportional to cell volume fraction and cell size. ΔR1,ss was correlated to Ktrans and higher in IDHwt-gliomas and treated gliomas, consistently with the hypothesis of ΔR1,ss being a potential marker of blood-brain barrier disruption. Preliminary results on non-enhancing gliomas suggest that leakage effect metrics are more likely to be visualized in higher grades, and may be insightful in this subset despite the absence of gross contrast-enhancement. Overall, leakage effect metrics may provide insights regarding tissue characteristics with potential applications in differential diagnosis and treatment response assessment.

TCL-154 STAT3 Mutations are Associated With Differential Treatment Responses to Methotrexate and Cyclophosphamide in T-Large Granular Lymphocyte Leukemia

TCL-154 STAT3 Mutations are Associated With Differential Treatment Responses to Methotrexate and Cyclophosphamide in T-Large Granular Lymphocyte Leukemia

POSTER: TCL-154 STAT3 Mutations are Associated With Differential Treatment Responses to Methotrexate and Cyclophosphamide in T-Large Granular Lymphocyte Leukemia

POSTER: TCL-154 STAT3 Mutations are Associated With Differential Treatment Responses to Methotrexate and Cyclophosphamide in T-Large Granular Lymphocyte Leukemia

LncRNAs associated with vascular mimicry establish a novel molecular subtype and prognostic model for pancreatic cancer.

Vascular mimicry (VM) epitomizes an innovative tumor angiogenesis pathway, potentially serving as an alternate conduit under the assumption of traditional tumor angiogenesis pathway inhibition. The role of VM in pancreatic cancer (PC), however, remains unexplored. Using differential analysis and Spearman correlation, we identified key long non-coding RNAs (lncRNAs) signatures in PC from the collected set of VM-associated genes in the literature. We identified optimal clusters using the non-negative matrix decomposition (NMF) algorithm, and then compared clinicopathological features and prognostic differences between clusters. We also assessed tumor microenvironmental (TME) differences between clusters using multiple algorithms. Using univariate Cox regression analyses as well as lasso regression, we constructed and validated new lncRNA prognostic risk models for PC. We used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze model-enriched functions and pathways. Nomograms were then developed to predict patient survival in association with clinicopathological factors. In addition, single-cell RNA-sequencing (scRNA-seq) analysis was used to analyze the expression patterns of VM-related genes and lncRNAs in the PC of TME. Finally, we used the Connectivity Map (cMap) database to predict local anaesthetics that could modify the VM of PC. In this study, we developed a novel three-cluster molecular subtype using the identified VM-associated lncRNA signatures of PC. The different subtypes have significantly different clinical characteristics and prognostic value, and also show differential treatment response and TME. Following an in-depth analysis, we constructed and validated a novel prognostic risk model for PC based on the VM-associated lncRNA signatures. Enrichment analysis suggested that high riskscores were significantly associated with functions and pathways, including extracellular matrix remodeling, et al. In addition, we predicted eight local anaesthetics that could modulate VM in PC. Finally, we discovered differential expression of VM-related genes and lncRNAs across various cell types within pancreatic cancer. VM has a critical role in PC. This study pioneers the development of a VM-based molecular subtype that demonstrates substantial differentiation in PC populations. Furthermore, we highlighted the significance of VM within the immune microenvironment of PC. Moreover, VM might contribute to PC tumorigenesis through its mediation of mesenchymal remodeling and endothelial transdifferentiation-related pathways, which offers a new perspective on its role in PC.