Adaptive Treatment Strategies Research Articles

Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations and thereby delay, or even prevent, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. Here, we applied deep reinforcement learning (DRL) to guide adaptive drug scheduling and demonstrated that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. The DRL strategies were robust to patient variability, including both tumor dynamics and clinical monitoring schedules. The DRL framework could produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. The DRL framework had no knowledge of the underlying mathematical tumor model, demonstrating the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, a proposed five-step pathway, which combined mechanistic modeling with the DRL framework and integrated conventional tools to improve interpretability compared to traditional "black-box" DRL models, could allow translation of this approach to the clinic. Overall, the proposed framework generated personalized treatment schedules that consistently outperformed clinical standard-of-care protocols.

Adaptive treatment strategy for a vestibular schwannoma in a patient with vascular Eagle syndrome: illustrative case.

Eagle syndrome, an uncommon condition, causes symptoms due to neural and/or vascular compression from an elongated styloid process or calcified stylohyoid ligament and can also complicate other planned surgical procedures. A 42-year-old female with loss of balance, dizziness, and ataxic gait underwent cranial magnetic resonance imaging (MRI), revealing a right-sided Koos grade IV vestibular schwannoma. Initially, a retrosigmoid craniotomy for tumor resection was planned. However, preoperative MRI and computed tomography (CT) showed a dilated right-sided mastoid emissary vein, tortuous scalp and paraspinal veins, and bilateral elongated styloid processes. CT angiography and digital subtraction angiography indicated Eagle syndrome-related compression of both internal jugular veins and concurrent occlusion of the left internal jugular vein at the jugular foramen. Consequently, given the risk of damaging venous structures, Gamma Knife radiosurgery was chosen over resection. This case highlights the importance of adapting treatment plans based on patient-specific anatomical and pathological factors. In situations in which traditional surgery poses risks to sensitive structures such as the venous system, alternative approaches like radiosurgery offer safer yet effective options. Comprehensive risk-benefit evaluations are crucial for such decisions.

Cancers of Unknown Primary Origin: Real-World Clinical Outcomes and Genomic Analysis at the European Institute of Oncology.

Cancer of unknown primary origin (CUP) poses a significant challenge due to poor clinical outcomes and limited treatment options. As such, further definition of clinicopathological factors and genomic profile to better adapt treatment strategies is required. Medical records were interrogated to retrospectively include CUP with available clinical and genomics data at the European Institute of Oncology. Next-generation sequencing (NGS) included targeted panels. Statistical analyses were conducted with R Software 4.2.2. A total of 44 patients were included. With a median follow-up of 39.46 months (interquartile range [IQR] 35.98-47.41 months), median PFS (mPFS) to first-line regimen was 3.98 months (95% CI 3.22-5.98), with a clinical benefit rate of 26% (95% CI 14%-49%), and disease control rate (DCR) limited to 48.28%. Most patients (26 of 31, 83.87%) received platinum-doublet chemotherapy, with no statistically significant difference between first-line treatment regimens. Median OS (mOS) was 18.8 months (95% CI 12.3-39.9), with a 12-month OS rate of 66% (95% CI 50%-85%). All patients received comprehensive genomic profiling (CGP). For 11 patients, NGS was unsuccessful due to low sample quantity and/or quality. For the remaining, TP53 (n = 16, 48%) and KRAS (n = 10, 30%) represented the most altered (alt) genes. No microsatellite instability was observed (0 of 28), while 6 of 28 (21.43%) tumors carried high TMB (≥10 mutation per megabase). Eight of 33 tumors (24.2%) displayed at least one actionable alteration with potential clinical benefit according to ESCAT. Only 2 of them received targeted therapy matched to genomic alterations, with a combined mPFS of 2.63 months (95% CI 1.84-not evaluable) as third-line regimens. Six patients received anti-PD1/PD-L1 therapy, showing a meaningful mPFS of 13 months (95% CI 2.04-not evaluable). CUP exhibits poor prognosis with limited benefits from standard treatment regimens. A significant proportion of CUPs carry actionable alterations, underscoring the importance of genomic profiling to gather additional treatment opportunities. In addition, immunotherapy might represent a valuable treatment option for a subset of CUP. Finally, accurate definition of sequencing methods and platforms is crucial to overcome NGS failures.

Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies.

Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized and effective disease management. Identification of key driver mutations and molecular profiling have deepened our comprehension of the genetic alterations in colorectal cancer, facilitating targeted therapy and immunotherapy selection. Biomarkers such as microsatellite instability (MSI) and DNA mismatch repair deficiency (dMMR) guide treatment decisions, opening avenues for immunotherapy. Emerging technologies such as liquid biopsies, artificial intelligence, and machine learning promise to revolutionize early detection, monitoring, and treatment selection in precision medicine. Despite these advancements, ethical and regulatory challenges, including equitable access and data privacy, emphasize the importance of responsible implementation. The dynamic nature of colorectal cancer, with its tumor heterogeneity and clonal evolution, underscores the necessity for adaptive and personalized treatment strategies. The future of precision medicine in colorectal cancer lies in its potential to enhance patient care, clinical outcomes, and our understanding of this intricate disease, marked by ongoing evolution in the field. The current reviews focus on providing in-depth knowledge on the various and diverse approaches utilized for precision medicine against colorectal cancer, at both molecular and biochemical levels.

A Short Update on the Use of Monoclonal Antibodies in COVID-19.

Monoclonal antibodies in the prophylaxis and treatment of COVID-19 have been crucial in reducing severe infections when vaccines were unavailable. However, as the virus and its variants have changed over time, the effectiveness of monoclonal antibodies has been questioned. This technical note highlights the need to assess the antiviral activity of these antibodies against new variants and adapt treatment strategies accordingly. On the one hand, in vitro studies have suggested reduced susceptibility of the latest variants to monoclonal antibodies, whereas clinical data still show benefits in reducing severe illness and mortality, indicating that laboratory results do not always mirror real-world outcomes. As a result, although resistance to monoclonal antibodies can develop over time, they could still have an important role in COVID-19 treatment, especially when used in combination, and ongoing research aims to identify effective antibodies against new variants.

Transdiagnostic and tailored internet intervention to improve mental health among university students: Research protocol for a randomized controlled trial

BackgroundEmerging adulthood is often associated with mental health problems. About one in three university students report symptoms of depression and anxiety that can negatively affect their developmental trajectory concerning work, intimate relationships, and health. This can interfere with academic performance, as mood and anxiety disorders are key predictors of dropout from higher education. A treatment gap exists, where a considerable proportion of students do not seek help for mood and anxiety symptoms. Offering internet interventions to students with mental health problems could reduce the treatment gap, increase mental health, and improve academic performance. A meta-analysis on internet interventions for university students showed small effects for depression and none for anxiety. Larger trials are recommended to further explore effects of guidance, transdiagnostic approaches, and individual treatment components.MethodsThis study will offer 1200 university students in Sweden participation in a three-armed randomized controlled trial (RCT) evaluating a guided or unguided transdiagnostic internet intervention for mild to moderate depression and anxiety, where the waitlist control group accesses the intervention at 6-month follow-up. Students reporting suicidal ideation/behaviors will be excluded and referred to treatment within the existing healthcare system. An embedded study within the trial (SWAT) will assess at week 3 of 8 whether participants in the guided and unguided groups are at higher risk of failing to benefit from treatment. Those at risk will be randomized to an adaptive treatment strategy, or to continue the treatment as originally randomized. Primary outcomes are symptoms of depression and anxiety. Follow-ups will occur at post-treatment and at 6-, 12-, and 24-month post-randomization. Between-group outcome analyses will be reported, and qualitative interviews about treatment experiences are planned.DiscussionThis study investigates the effects of a transdiagnostic internet intervention among university students in Sweden, with an adaptive treatment strategy employed during the course of treatment to minimize the risk of treatment failure. The study will contribute knowledge about longitudinal trajectories of mental health and well-being following treatment, taking into account possible gender differences in responsiveness to treatment. With time, effective internet interventions could make treatment for mental health issues more widely accessible to the student group.

Abstract PR001: Learning to adapt: Rational personalization of adaptive therapy using deep reinforcement learning

Abstract Standard-of-care treatment regimens have long been designed for maximal cell kill, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations, delaying, or even preventing, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. We propose the application of deep reinforcement learning (DRL) to guide adaptive drug scheduling, and demonstrate that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. We show that the DRL strategies are robust to patient variability, including both tumor dynamics and clinical monitoring schedules. We demonstrate that the DRL framework can produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. Given the DRL framework has no knowledge of the underlying mathematical tumor model, this demonstrates the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, we propose a five-step pathway combining mechanistic modeling with the DRL framework to translate this approach to the clinic with limited information for a given patient. Integrating conventional tools to ensure the interpretability of traditionally `black-box' DRL models, we generate personalized treatment schedules that consistently outperform clinical standard-of-care protocols. Citation Format: Kit Gallagher, Maximilian Strobl, Robert Gatenby, Philip Maini, Alexander Anderson. Learning to adapt: Rational personalization of adaptive therapy using deep reinforcement learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR001.

Situational Awareness in the Context of Clinical Practice

In the context of clinical practice, situational awareness refers to conscious awareness (knowledge), which is a mental model of a given clinical situation in terms of its elements and the significance of their interrelation. Situational awareness (SA) facilitates clinical reasoning, diagnostic accuracy, and appropriate goal-directed performance, and it enables clinicians to immediately adapt treatment strategies in response to changes in clinical situational actualities and to modify the course of goal-directed activities accordingly. It also helps clinicians prepare future operational plans and procedures based on the projection of situational developments. SA, therefore, is an important prerequisite for safe clinical procedures. The purpose of this narrative review is to highlight certain cognitive and external (environmental) situational factors that influence the development of situational awareness. Understanding the dynamic, adaptive, and complex interactions between these factors may assist clinicians and managers of healthcare systems in developing methods aimed at facilitating the acquisition of accurate clinical situational awareness and, in turn, may bring about a reduction in the incidence of SA, diagnostic, and operational errors.

Prognostic Factors Impacting Post-Transplant Outcomes in Adult T-Cell Acute Lymphoblastic Leukemia: A Registry-Based Study By the EBMT Acute Leukemia Working Party

Background: T-cell acute lymphoblastic leukemia (T-ALL) predominantly affects individuals in late childhood and young adulthood and historically had high mortality rates. The outcome of Pediatric inspired chemo protocols in the T-ALL have improved in the past years due to the advancement in treatment and supportive care but the data is less mature, although adult patients still face challenges. Limited studies have directly explored the impact of patient- and transplant-related factors on post-transplant outcomes in adult T-ALL patients after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods: A retrospective registry-based analysis was conducted across multiple centers using data from the European Society for Blood and Marrow Transplantation (EBMT) registry. Inclusion criteria were adult T-ALL patients (>18 years old) who underwent their first allo-HSCT in first complete remission from matched sibling donors (MSD), unrelated donors (UD) (10/10 or 9/10) or haploidentical donors between 2010 and 2021. Cord bloods were excluded. Multivariable Cox regression analysis was performed to examine the associations between patient/transplant characteristics and outcomes. Results: A total of 1907 patients were included in the study. The median age at transplant was 33.4 years (18.1-75). The median follow up was 2.9 years (2.6-3.1), and the median year of HSCT was 2016 (2010-2021). Patients were 70 % male with 67% being CMV-positive, at the time of transplant. Donors were predominantly male (64%), and (23%) of transplants involved female donors to male patients. The median period between diagnosis and HSCT was 5.9 months (0 -23.8). Most transplants were from matched sibling donors (MSD) (45%) followed by UD (43%) and 12% were from Haploidentical transplants. Most patients underwent myeloablative conditioning with total body irradiation (TBI)-based regimens (69%) and most patients received peripheral blood stem cells (84%). Cyclosporine with methotrexate was the most common graft-versus-host-disease (GVHD) prophylaxis (54 %). In vivo T cell depletion was used in 44%. The 2-year overall survival (OS) was 69.4%, and leukemia -free survival (LFS) was 62.1%. The cumulative incidence of acute GVHD (aGVHD) grades II-IV at 100 days was 32.6%, and grades III-IV was 10%. The cumulative incidence of chronic GVHD (cGVHD) at 2 years was 37.3%, and extensive cGVHD was 16.8%. Multivariate analysis yielded significant associations between patient/transplant characteristics and outcomes. Advanced age at transplant was associated with poorer outcomes, including LFS (HR=1.11, p=0.004), GVHD-free, relapse-free survival (GRFS) (HR=1.06, p=0.04), OS (HR=1.12, p=0.002), and higher non-relapse mortality (NRM) (HR=1.23, p=0.001); HR for 10y increment. A later year of HSCT was associated with improved GFRS (HR=0.89, p=0.001), OS (HR=0.9, p=0.02), and decreased NRM (HR=0.82, p=0.008), aGVHD II-IV (HR=0.83, p=0.001), and cGVHD (HR=0.8, p=0.001); HR for 3y increment. TBI was specifically superior with improved LFS (HR=0.79, p=0.02), GRFS (HR=0.83, p=0.04), and lower relapse incidence (RI) (HR=0.65, p=0.001). Female-to-male transplant combination had a negative impact on GFRS (HR=1.21, p=0.02), OS (HR=1.23, p=0.048), and on the risk of cGVHD in general (HR=1.39, p=0.002) and specifically extensive cGVHD (HR=1.47, p=0.01). The use of in vivo T-cell depletion had significant benefits in terms of GFRS, aGVHD grade III-IV, cGVHD, and extensive cGVHD. Conclusion: This large study identified prognostic factors, such as age at transplant, donor type, and conditioning regimen, in influencing key outcomes including OS, LFS, GVHD incidence, and NRM in adult T-ALL patients undergoing allo-HSCT. Importantly, a significant improvement over time in post-transplant outcomes was noted. These findings hold great promise for new adapted treatment strategies and can serve as a benchmark for future studies in that setting.

Machine Learning Based Prediction of Postoperative Pancreatic Fistula Following Pancreaticoduodenectomy.

To develop a novel machine learning (ML) model to predict clinically relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy (PD). Accurate prognostication of CR-POPF may allow for risk stratification and adaptive treatment strategies for potential PD candidates. However, antecedent models, such as the modified Fistula Risk Score (mFRS), are limited by poor discrimination and calibration. All records entailing PD within the 2014-2018 ACS NSQIP were identified. Additionally, patients undergoing PD at our institution between 2013 and 2021 were queried from our local data repository. An eXtreme Gradient Boosting (XGBoost) model was developed to estimate the risk of CR-POPF using data from the ACS NSQIP and evaluated using institutional data. Model discrimination was estimated using the area under the receiver operating characteristic (AUROC) and precision recall curve (AUPRC). Overall, 12,281 and 445 patients undergoing PD were identified within the 2014-2018 ACS NSQIP and our institutional registry, respectively. Application of the XGBoost and mFRS scores to the internal validation dataset revealed that the former model had significantly greater AUROC (0.72 vs. 0.68, P<0.001) and AUPRC (0.22 vs. 0.18, P<0.001). Within the external validation dataset, the XGBoost model remained superior to the mFRS with an AUROC of 0.79 (95% CI 0.74-0.84) versus 0.75 (95% CI 0.70-0.80, P<0.001). In addition, AUPRC was higher for the XGBoost model, compared to the mFRS. Our novel ML model consistently outperformed the previously validated mFRS within internal and external validation cohorts, thereby demonstrating its generalizability and utility for enhancing prediction of CR-POPF.

IVY: protocol for a randomised clinical trial to test the effect of a technology-based intervention to improve virological suppression among young adults with HIV in the USA

IntroductionYoung adults with HIV (YWH) experience worse clinical outcomes than adults and have high rates of substance use (SU) and mental illness that impact their engagement in care and adherence...

Evaluation of the Possibility of Dose Realignment Adaptation by Shifting the Isocenter in Proton Beam Therapy for Pancreatic Cancer

In pancreatic cancer, a tumor is surrounded by the gastrointestinal (GI) tract, which is subject to changes in location, shape, and contents. Due to these inter-fractional changes, proton beam therapy (PBT) for pancreatic cancer may result in unintentionally high doses to the GI tract. Daily adaptive re-planning can solve this problem, but is not yet established with PBT due to its resource intensive characteristics. This study aims to evaluate the GI tract dose using weekly computed tomography (CTw) and the possibility of dose realignment adaptation by shifting the isocenter (IC) of the PBT plan, which does not require re-planning. We retrospectively analyzed 6 consecutive patients with unresectable pancreatic cancer treated with real-time-image gated PBT using a fiducial marker. The planning CT was scanned at the natural expiration of respiration and a PBT plan of 60 GyE in 25 fractions (baseline plan, PLANbase) was created. The CTw images were acquired the day before start of PBT and once a week during the PBT course thereafter. The PLANbase was rigidly transferred to the CTw based on the relationship between the three-dimensional coordinates of the fiducial marker and those of the IC in the PLANbase. The PLANeval was created by recalculating the PLANbase on the CTw. We evaluated the doses to the stomach, duodenum, and intestines in the PLANeval according to the following criteria: Dmax of the stomach < 60 GyE, duodenum and intestines < 55 GyE, and D1cc of the stomach < 55 GyE, duodenum and intestines < 54 GyE. In addition, we investigated the GI tract dose realignment adaptation for the PLANeval with its IC shifted 2mm, 4mm, and 6mm in each of 6 directions (right, left, ventral, dorsal, cranial, and caudal), respectively. A total of 35 PLANeval were created for the CTw. In the PLANbase of the 6 patients, the average of Dmax and D1cc of the stomach, duodenum and intestines were 50.7 GyE (range, 46.7-53.6) and 50.0 GyE (45.0-53.2), 49.2 GyE (44.3-51.7) and 48.8 GyE (43.7-51.5), and 49.2 GyE (44.8-52.0) and 48.9 GyE (44.6-51.8), respectively. In the PLANeval, the average of Dmax and D1cc of the stomach, duodenum, and intestines were 53.3GyE (43.8-61.4) and 52.8 GyE (43.2-61.1), 51.0 GyE (36.1-60.0) and 50.3 GyE (35.4-59.8), and 52.5 GyE (36.6-61.0) and 51.9 (34.4-60.9) GyE, respectively. Twenty-two of the 35 PLANeval (63 %) did not meet at least one of the GI tract dose criteria. In 11 of 22 PLANeval with higher doses to the GI tract, the IC shift resulted in GI dose reductions and all dose criteria were met. The minimum amount of the IC shift required to meet the criteria was 2 mm for 8 plans and 4 mm for 3 plans. The remaining 11 PLANeval did not meet the criteria using dose realignment adaptation by shifting the isocenter. Adaptive replanning is necessary for PBT for pancreatic cancers due to excessive GI tract doses in more than 60% of the plans. Dose realignment adaptation by shifting the IC, which does not require re-planning, may be an option in adaptive treatment strategies.

[The Erlangen model of tinnitus development-New perspective and treatment strategy].

About one sixth of the population of western industrialized nations suffers from chronic, subjective tinnitus, causing socioeconomic treatment and follow-up costs of almost 22billion euros per year in Germany alone. According to the prevailing view, tinnitus develops as aconsequence of amaladaptive neurophysiological process in the brain triggered by hearing loss. The Erlangen model of tinnitus development presented here is intended to propose acomprehensive neurophysiological explanation for the initial occurrence of the phantom sound after hearing loss. Based on the model, anew treatment strategy will be developed. The model summarized here is based on various animal and human physiological studies conducted in recent years. The Erlangen model considers subjective tinnitus as aside effect of aphysiological mechanism that permanently optimizes information transmission into the auditory system by means of stochastic resonance (SR) even in the healthy auditory system. In fact, hearing-impaired patients with tinnitus hear better on average than those without tinnitus. This unfamiliar perspective on the phantom percept may already help affected patients to cope better with their suffering. In addition, based on the model, low intensity noise tinnitus suppression (LINTS) has been developed as anew, individually adapted treatment strategy for tonal tinnitus and has already been successfully tested in patients. Apossible limiting factor for the model and treatment strategy is the pitch of the tinnitus percept, which may require adjustments to the treatment strategy for frequencies above about 5 kHz.

Machine learning applications and challenges in graft-versus-host disease: a scoping review.

This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, "snapshot" assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.

Early indicators of response to transdiagnostic treatment of pediatric anxiety and depression.

Pediatric anxiety and depression are prevalent, impairing, and highly comorbid. Available evidence-based treatments have an average response rate of 60%. One path to increasing response may be to identify likely non-responders midway through treatment to adjust course prior to completing an episode of care. The aims of this study, thus, were to identify predictors of post-intervention response assessing (a) mid-treatment symptom severity, (b) session-by-session treatment process factors, and (c) a model optimizing the combination of these. Data were drawn from the treatment arm (N = 95, ages 8-16) of a randomized transdiagnostic intervention trial (Msessions  = 11.2). Mid-point measures of youth- and parent-reported anxiety and depression were collected, and therapists rated homework completion, youth and parent engagement, and youth therapeutic alliance at each session. Logistic regression was used to predict response on the Clinical Global Impression Improvement Scale (CGI-I ≤2) rated by independent evaluators masked to treatment condition. Mid-point symptom measures were significant predictors of treatment response, as were therapist-ratings of youth and parent engagement, therapeutic alliance, and homework completion. Therapist ratings were significant when tested as mean ratings summing across the first eight sessions of treatment (all ps < .004) and at individual session points (all ps <0.05). A combined prediction model included youth-reported anxiety, parent-reported depression, youth engagement at Session 2, and parent engagement at Session 8. This model correctly classified 76.5% of youth as non-responders and 91.3% as responders at post-treatment (Nagelkerke R2  = .59, χ2 (4, 80) = 46.54, p < .001). This study provides initial evidence that response to transdiagnostic intervention for pediatric anxiety and depression may be reliably predicted by mid-point. These data may serve as foundational evidence to develop adaptive treatment strategies to personalize intervention, correct treatment course, and optimize outcomes for youth with anxiety and depression.

Significance of coping strategies on the outcome of surgical treatment of patients with prosoplegia

Aim: to evaluate the impact of coping strategies on the outcomes of surgical treatment of patients with prosoplegia. Material and methods: the analysis of the results of surgical treatment of 50 patients with prosoplegia that developed after removal of tumors of the posterior part of the skull, the lateral part of the skull base, and the parotid-masticatory region involved in reconstructive interventions on the facial nerve was performed. At the preoperative stage a clinical examination of patients was performed according to the House-Brackmann scale, Sunnybrook Facial Grading System (SFGS) scale, and a neurophysiological study was performed. A survey was carried out with the use of the Heim’s questionnaire, WCQ (version by Lazarus), CSI as an indicator of coping strategies before surgery and the Aaron Antonovsky’s Sense of Coherence Scale (SOC). Outcomes were evaluated according to the Johnny Chuieng–Yi Lu subjective scale “Satisfaction with surgical treatment”. Results: all patients from 6 to 10 months after surgery showed signs of muscle recovery, which in the majority (88%, n=44) was accompanied by an improvement in their condition. In 12% of cases (n=6) there were no positive dynamics, which caused unsatisfactory results of treatment. Such painful conditions are often interrupted or recommendations for self-rehabilitation are not followed. The above-described predominance of maladaptive coping strategies in patients with unsatisfactory initial operations is reliable. Adaptive treatment strategies were better developed in patients with favorable treatment outcomes. A high direct correlation was established between the outcomes of treatment, assessed by the Johnny Chuieng–Yi Lu scales, SGFS and SOC coherence scale, Conclusion: Systemic complex rehabilitation and self-rehabilitation have a significant impact on treatment outcomes. Prosoplegia leads to a violation of facial and emotional expression, causing social maladjustment and depressive disorders, minimizing the effects of reinnervation in combination with negative coping strategies. As a result, full-fledged surgical treatment of the facial muscles is currently a very difficult and sometimes insurmountable task.

Use of a Statistical Adaptive Treatment Strategy Approach for Emulating Randomized Controlled Trials Using Observational Data: The Example of Blood-Pressure Control Strategies for the Prevention of Cardiovascular Events Among Individuals With Hypertension at High Cardiovascular Risk.

Statistical approaches to adaptive treatment strategies (ATS) can be used to mimic the sequential decision-making inherently found in clinical practice. To illustrate the use of a statistical ATS approach, we emulated a target trial of different blood pressure (BP) control plans for the prevention of cardiovascular events among individuals with hypertension at high cardiovascular risk, inspired by the Systolic Blood Pressure Intervention Trial (SPRINT). We included 103,708 patients with hypertension and a "QRISK3" estimated 10-year risk of cardiovascular disease of ≥20% who initiated an antihypertensive drug between 1998 and 2018. Dynamic marginal structural models estimated the comparative effects of treating patients with intensive (target BP: 130/80 mm Hg), standard (140/90 mm Hg), and conservative (150/90 mm Hg) BP control strategies. The adjusted hazard ratios (HRs) for the intensive versus standard strategy were 0.96 (95% confidence interval (CI): 0.92, 1.00) for major adverse cardiovascular events and 0.93 (95% CI: 0.88, 0.97) for death from cardiovascular causes. For the conservative versus standard strategy, they were 1.06 (95% CI: 1.02, 1.10) and 1.08 (95% CI: 1.03, 1.13), respectively. These results are largely compatible with SPRINT. ATS can be used to emulate randomized controlled trials of complex treatment strategies in an observational setting and represents an alternative approach for situations where randomized controlled trials are not feasible.

Can transbronchial lung cryobiopsy benefit adaptive treatment strategies in connective tissue disease-associated interstitial lung disease?

BackgroundSome patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD) progress to pulmonary fibrosis over their disease course despite initial improvement, potentially indicating a poor prognosis. Transbronchial lung cryobiopsy (TBLC) is a new bioptic approach used in diffuse parenchymal lung diseases. This study of CTD-ILD assessed the utility of TBLC in determining therapeutic decision-making strategies.MethodsWe analyzed medical records of 31 consecutive CTD-ILD patients who underwent TBLC focusing on radio-pathological correlation and disease course. A TBLC-based usual interstitial pneumonia (UIP) score was used that assessed three morphologic descriptors: i) patchy fibrosis, ii) fibroblastic foci, and iii) honeycombing.ResultsAmong the patients with CTD-ILD, 3 had rheumatoid arthritis, 2 systemic sclerosis, 5 polymyositis/dermatomyositis, 8 anti-synthetase syndrome, 6 Sjögren’s syndrome, and 5 had microscopic polyangiitis. Pulmonary function test results showed a mean %FVC of 82.4% and %DLCO of 67.7%. Among the 10 CTD patients and TBLC-proven pathological UIP, 3 patients had prominent inflammatory cells in addition to a framework of UIP, and pulmonary function of most patients improved with anti-inflammatory agents. Six (40%) of 15 patients with TBLC-based UIP score ≥ 1 had a progressive disease course during follow-up, of whom 4 patients received anti-fibrotic agents.ConclusionsTBLC in patients with CTD-ILD can help determine an appropriate medication strategy, particularly when UIP-like lesions are present. TBLC may be useful when judging which agents to prioritize, anti-inflammatory or anti-fibrotic, is difficult. Moreover, additional information from TBLC may be beneficial when considering early intervention with anti-fibrotic agents in clinical practice.

Effect of Problem Area on Outcomes Among Adolescents Treated With Interpersonal Psychotherapy for Depression.

Interpersonal psychotherapy for adolescents (IPT-A) aims to treat depression by addressing one of four problem areas: grief, role disputes, role transitions, or interpersonal deficits. This study compared the characteristics of adolescents by problem area and evaluated the impact of problem area on outcomes. Forty adolescents (ages 12-17) participated in a randomized trial of adaptive treatment strategies that included IPT-A. Adolescents with role disputes were younger than adolescents with interpersonal deficits or role transitions and had worse expectations for therapeutic alliance than those in the transitions group. Adolescents with interpersonal deficits had higher attachment avoidance than those in the transitions group. Posttreatment, adolescents in the transitions group had more severe depression and social adjustment problems than those in the deficits group and more social adjustment problems than those in the disputes group. This preliminary study's findings suggest that differing IPT-A problem areas may affect prognosis.

Abstract 3164: Disulfiram re-sensitizes ER+ breast cancer cells resistant to chemotherapy through an evolutionary double-bind

Abstract Most ER+ metastatic breast cancer patients develop resistance to endocrine therapy and are also typically cross-resistant to chemotherapy, resulting in disease progression. However, chemotherapy resistant lines have been shown to acquire cross-sensitivity to therapies such as disulfiram, a drug used to treat alcoholism. Because of this cross-sensitivity, we hypothesized the existence of an “evolutionary double-bind”, a situation where treatment with one drug improves the response to a different agent. This can occur when resistance mechanisms rewire key proliferative pathways, creating potentially targetable vulnerabilities to be exploited. While recent studies have applied theoretical ecological and evolutionary principles in understanding cancer progression, it remains a new and exciting endeavor to quantify effective evolutionary games in refractory cancer to design alternative treatment strategies that induce an evolutionary double-bind. Here, we developed ER+ breast cancer cell lines sensitive and resistant to paclitaxel or doxorubicin to test the cross-sensitivity and efficacy of chemotherapy and disulfiram as an alternative treatment strategy. To quantify this cross-sensitivity of chemotherapy and disulfiram and use the double-bind to mitigate resistance, we also developed a game-theoretic mathematical modeling framework with parametrization performed using the “evolutionary game assay” technique, a direct measurement of payoff matrices that specify evolutionary games measured in cancer. Spheroids of varied initial frequencies of sensitive and resistant cells (100% monoculture; 75/25, 50/50, and 25/75 sensitive and resistant coculture) were treated with chemotherapy and disulfiram. The mathematical model was parameterized from the in vitro experimental data and as expected, treating with chemotherapy selects for resistant cells. However, disulfiram alone shifts the selection dynamics to favor sensitive cells. These modeling results predict the existence of a double-bind between chemotherapy and disulfiram where disulfiram selects for the sensitive subpopulation, which then re-sensitizes the spheroids to subsequent administration of chemotherapy. Importantly, the model predicts that the double-bind is non-symmetric and does not exist when treatments are given in combination. These predictions suggest the importance of timing/dosing strategies in which several schemes were further validated in vitro, confirming the superior efficacy of chemotherapy followed by disulfiram in blocking resistance emergence. This project integrates experimental data with mathematical modeling to identify a treatment double-bind in ER+ breast cancer. The integrative experimental-mathematical framework developed here has broad applicability in designing evolution-based or adaptive therapy treatment strategies across a wide range of cancer types. Citation Format: Rena Emond, Jeffrey West, Alexander Anderson, Andrea H. Bild. Disulfiram re-sensitizes ER+ breast cancer cells resistant to chemotherapy through an evolutionary double-bind [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3164.