Spatial colocalization and combined survival benefit of natural killer and CD8 T cells despite profound MHC class I loss in non-small cell lung cancer.

Background: MHC class I (MHC-I) loss is frequent in non-small cell lung cancer (NSCLC) rendering tumor cells resistant to T cell lysis. NK cells kill MHC-I-deficient tumor cells, and although previous work indicated their presence at NSCLC margins, they were functionally impaired. Within, we evaluated whether NK cell and CD8 T cell infiltration and activation vary with MHC-I expression. Methods: We used single-stain immunohistochemistry (IHC) and Kaplan-Meier analysis to test the effect of NK cell and CD8 T cell infiltration on overall and disease-free survival. To delineate immune covariates of MHC-I-disparate lung cancers, we used multiplexed immunofluorescence (mIF) imaging followed by multivariate statistical modeling. To identify differences in infiltration and intercellular communication between IFNγ-activated and non-activated lymphocytes, we developed a computational pipeline to enumerate single cell neighborhoods from mIF images followed by multivariate discriminant analysis. Results: Spatial quantitation of tumor cell MHC-I expression revealed intra- and inter-tumoral heterogeneity, which was associated with the local lymphocyte landscape. IHC analysis revealed that high CD56+ cell numbers in patient tumors were positively associated with disease-free survival (DFS) (HR=0.58, p=0.064) and overall survival (OS) (HR=0.496, p=0.041). The OS association strengthened with high counts of both CD56+ and CD8+ cells (HR=0.199, p<1×10-3). mIF imaging and multivariate discriminant analysis revealed enrichment of both CD3+CD8+ T cells and CD3-CD56+ NK cells in MHC-I-bearing tumors (p<0.05). To infer associations of functional cell states and local cell-cell communication, we analyzed spatial single cell neighborhood profiles to delineate the cellular environments of IFNγ+/- NK cells and T cells. We discovered that both IFNγ+ NK and CD8 T cells were more frequently associated with other IFNγ+ lymphocytes in comparison to IFNγ- NK cells and CD8 T cells (p<1×10-30). Moreover, IFNγ+ lymphocytes were most often found clustered near MHC-I+ tumor cells. Conclusions: Tumor-infiltrating NK cells and CD8 T cells jointly affected control of NSCLC tumor progression. Co-association of NK and CD8 T cells was most evident in MHC-I-bearing tumors, especially in the presence of IFNγ. Frequent co-localization of IFNγ+ NK cells with other IFNγ+ lymphocytes in near-neighbor analysis suggests NSCLC lymphocyte activation is coordinately regulated.

Peripheral positions encode transport specificity in the small multidrug resistance exporters.

In secondary active transporters, a relatively limited set of protein folds have evolved diverse solute transport functions. Because of the conformational changes inherent to transport, altering substrate specificity typically involves remodeling the entire structural landscape, limiting our understanding of how novel substrate specificities evolve. In the current work, we examine a structurally minimalist family of model transport proteins, the small multidrug resistance (SMR) transporters, to understand the molecular basis for the emergence of a novel substrate specificity. We engineer a selective SMR protein to promiscuously export quaternary ammonium antiseptics, similar to the activity of a clade of multidrug exporters in this family. Using combinatorial mutagenesis and deep sequencing, we identify the necessary and sufficient molecular determinants of this engineered activity. Using X-ray crystallography, solid-supported membrane electrophysiology, binding assays, and a proteoliposome-based quaternary ammonium antiseptic transport assay that we developed, we dissect the mechanistic contributions of these residues to substrate polyspecificity. We find that substrate preference changes not through modification of the residues that directly interact with the substrate but through mutations peripheral to the binding pocket. Our work provides molecular insight into substrate promiscuity among the SMRs and can be applied to understand multidrug export and the evolution of novel transport functions more generally.

How Variable are Patient Comorbidity Profiles Among Practicing Otolaryngologists?

OBJECTIVE: To determine whether certain groups of otolaryngologists (ORLs) are treating cohorts of patients with more comorbidities. STUDY DESIGN: Cross-sectional population-based analysis. SETTING: 2019 Medicare Provider Utilization and Payment Dataset. METHODS: Each ORL's average Medicare hierarchical condition category (HCC) risk score, a comorbidity index calculated from a patient's comorbidities, was collected. These were stratified and compared by various physician characteristics, including practice region and rurality, years in practice, gender, subspecialty, and setting (academic vs community). RESULTS: Among 8959 ORLs, the mean HCC risk score for Medicare patients was 1.35 ± 0.35. On univariate analysis, ORLs practicing in urban (compared to rural), ORLs in academic settings (compared to community), and early career ORLs all had a patient population with a higher HCC risk score (P < .001 for all). On multivariate analysis controlling for gender, rurality, graduation year, and region, rural setting was associated with decreased odds of having a high-risk patient population (odds ratio: 0.58 [95% confidence interval, CI: 0.48-0.71]; P < .001), while those more recently graduated has an increased risk (2000-2009: 1.41 [1.01-1.96], P = .046; 2010-2015: 2.30 [1.63-3.25], P < .001). In a separate subgroup analysis, subspecialty differences were seen and community setting was associated with decreased odds of having a high-risk patient population (0.36 [0.23-0.55]; P < .001). CONCLUSION: There is variability in patient comorbidity profiles among ORLs, with those in urban settings, those more recently graduated, and those in academic settings treating a group with more comorbidities. As the comorbidity burden may increase the cost of practice and complications, these findings may have important implications for health inequity.

Current issues in dose-finding designs: A response to the US Food and Drug Adminstration's Oncology Center of Excellence Project Optimus.

With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue.

Applications of the partial-order continual reassessment method in the early development of treatment combinations.

Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer.

Venetoclax with decitabine or azacitidine in relapsed or refractory acute myeloid leukemia.

Relapsed or refractory acute myeloid leukemia (AML) is associated with poor outcomes and resistance to therapy. The addition of venetoclax, a BCL-2 antagonist, to lower-intensity therapies results in improved survival in the first-line setting compared to monotherapy with a hypomethylating agent or low-dose cytarabine. Despite this, much remains unknown about the performance of venetoclax with a hypomethylating agent following the first-line setting. Additionally, while the ELN 2022 guidelines appear to improve the prognostication of AML, clarification is needed to determine how the revision applies to lower-intensity strategies. To investigate this, we retrospectively analyzed the performance of venetoclax with decitabine or azacitidine in relapsed or refractory AML under the ELN 2022 guidelines. We demonstrated that the ELN 2022 revision is not optimized for lower-intensity venetoclax-based strategies. To refine the prognostication schema, we showed significantly improved response and survival benefits for patients with mutated NPM1 and IDH. Relatively, patients with mutated NRAS, KRAS, and FLT3-ITD were associated with inferior response and survival. Furthermore, there is an unmet clinical need for tools to improve the selection of lower-intensity therapy candidates with borderline functional status. Using an incremental survival computation method, we discovered that a CCI score threshold of 5 distinguishes patients at an elevated risk of death. Together, these novel findings highlight areas of refinement to improve survival in relapsed or refractory AML.

A Pilot Study of the Combination of Entinostat with Capecitabine in Advanced Breast Cancer.

Background: Breast cancer has an unacceptably high recurrence rate when any residual disease is found following neoadjuvant treatment of high-risk disease. Based on clinical data suggesting an adjuvant role for epigenetic modifying agents in breast cancer and preclinical data suggesting synergistic activity of entinostat combined with capecitabine, we conducted a phase I, open-label study of these agents in metastatic breast cancer (MBC). Both agents have published doses for use in combination therapy, but the agents had not previously been combined with each other in a human trial. Methods: A multisite phase I dose escalation study was performed at two academic centers. Patients with pretreated, HER2-negative MBC, and measurable disease were enrolled. Dual dose escalation was performed via a Bayesian partial order continual assessment method. Dose levels ranged from entinostat 3 mg to 5 mg and capecitabine 800 mg/m2 to 1000 mg/m2. Results: Thirteen patients with MBC and a median of 4 lines of prior therapy were enrolled across four dose level combinations. The most common toxicities were neutropenia, thrombocytopenia, and palmar-plantar dysesthesia, which were expected toxicities. No new safety signals were observed. One dose-limiting toxicity was observed, which did not exceed a prespecified toxicity rate of 25%. The median treatment duration was 2.37 months. No partial nor complete responses were observed. The study was halted early prior to entering an expansion phase, due to drug supply limitations. Conclusion: The tested dosing combinations of entinostat and capecitabine are likely safe in heavily pretreated metastatic breast cancer. This study's clinical investigation of entinostat in breast cancer was halted, but drug development of this agent continues outside the US. There remains a need for postoperative adjuvant drug therapy for the subpopulation of breast cancer patients with high-risk residual cancer after curative therapy. This trial is registered with NCT03473639.

Repopulated spinal cord microglia exhibit a unique transcriptome and contribute to pain resolution.

Microglia are implicated as primarily detrimental in pain models; however, they exist across a continuum of states that contribute to homeostasis or pathology depending on timing and context. To clarify the specific contribution of microglia to pain progression, we take advantage of a temporally controlled transgenic approach to transiently deplete microglia. Unexpectedly, we observe complete resolution of pain coinciding with microglial repopulation rather than depletion. We find that repopulated mouse spinal cord microglia are morphologically distinct from control microglia and exhibit a unique transcriptome. Repopulated microglia from males and females express overlapping networks of genes related to phagocytosis and response to stress. We intersect the identified mouse genes with a single-nuclei microglial dataset from human spinal cord to identify human-relevant genes that may ultimately promote pain resolution after injury. This work presents a comprehensive approach to gene discovery in pain and provides datasets for the development of future microglial-targeted therapeutics.

Dose-finding based on feasibility and late-onset toxicity in adoptive cell therapy trials.

Cell therapies comprise one of the most important advances in oncology. One of the biggest challenges in the early development of cell therapies is to recommend safe and feasible doses to carry forward to middle development. The treatment involves extracting cells from a patient, expanding the cells and infusing the cells back into the patient. Each dose level being studied is defined by the number of cells infused into the trial participant. The manufacturing process may not generate enough cells for a given patient to receive their assigned dose level, making it infeasible to administer their intended dose. The primary design challenge is to efficiently use accumulated data from participants treated away from their assigned dose to efficiently allocate future trial participants and recommend a feasible maximum tolerated dose (FMTD) at the study conclusion. Currently, there are few available options for designing and implementing Phase I trials of cell therapies that can incorporate a dose feasibility endpoint. Moreover, the application of these designs is limited to a traditional dose-finding framework, where the dose-limiting toxicity (DLT) endpoint is observed in early cycles of therapy. This paper presents a novel phase I trial design for adoptive cell therapy that simultaneously accounts for dose feasibility and late-onset toxicities. We apply our design to a phase I dose-escalation trial of Rituximab-based bispecific activated T-cells combined with a fixed dose of Nivolumab. Our simulation results demonstrate that our proposed method can reduce trial duration without significantly hindering trial accuracy.

Impact of facility volume on survival in primary endoscopic surgery for sinonasal squamous cell carcinoma.

OBJECTIVES: To evaluate the impact of facility volume on outcomes following primary endoscopic surgical management of sinonasal squamous cell carcinoma (SNSCC). METHODS: The 2010-2016 National Cancer DataBase (NCDB) was queried for patients diagnosed with T1-T4a SNSCC surgically treated endoscopically as the primary treatment modality. Factors associated with overall survival (OS) were evaluated, including facility volume. RESULTS: A total of 330 patients who underwent endoscopic surgical management of SNSCC were treated at 356 unique facilities designated as either low-volume (LVC; treating 1-2 cases; 0-75th percentile), intermediate-volume centers (IVC; 3-4 cases total; 75th-90th percentile), or 144 high-volume (HVC; treating 5+ cases total; >90th percentile) centers. HVC treated patients with higher T staging (42.1 % vs. 29.8 %) and tumors in the maxillary sinus (26.9 % vs. 13.2 %) and ethmoid sinus (10.3 % vs. ≤8.3 %), while LVCs treated lower T stage tumors (70.2 % vs. 57.9 %) and tumors that were located in the nasal cavity (70.2-78.5 % vs. 62.8 %). On multivariable analysis, factors associated with decreased OS included higher T stage (T3/T4a vs. T1/T2; OR 1.92, 95 % CI 1.06-3.47) and older age (>65 vs. <65; OR 2.69, 95 % CI 1.62-4.49). Cases treated at high-volume centers were not associated with a higher likelihood of OS when compared to low-volume centers (OR 0.70, 95 % CI 0.36-1.35). CONCLUSIONS: HVC are treating more primary tumors of the maxillary and ethmoid sinuses and tumors with higher T stages with endoscopic approaches, although this does not appear to be associated with increased OS. SHORT SUMMARY: Sinonasal squamous cell carcinoma (SNSCC) presents late in disease process with poor prognosis. We investigated the impact of facility volume on outcomes following endoscopic treatment of SNSCC. High-volume centers treat more advanced and complex disease with comparable OS.

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study.

Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.

Local continual reassessment methods for dose finding and optimization in drug-combination trials.

Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. We propose to employ local, underparameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.

Concurrent dose-finding of a novel cancer drug with and without a second agent.

Introduction: More complex research questions are being posed in early-phase oncology clinical trials, necessitating design strategies tailored to contemporary study objectives. This paper describes the proposed design of a Phase I trial concurrently evaluating the safety of a hematopoietic progenitor kinase-1 inhibitor (Agent A) as a single agent and in combination with an anti-PD-1 agent in patients with advanced malignancies. The study's primary objective was to concurrently determine the maximum tolerated dose (MTD) of Agent A with and without anti-PD-1 therapy among seven possible study dose levels. Methods: Our solution to this challenge was to apply a continual reassessment method shift model to meet the research objectives of the study. Results: The application of this method is described herein, and a simulation study of the design's operating characteristics is conducted. This work was developed through collaboration and mentoring between the authors at the American Association for Cancer Research (AACR) and the American Society of Clinical Oncology (ASCO) annual AACR/ASCO Methods in Clinical Cancer Research Workshop. Conclusions: The aim of this manuscript is to highlight examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying modern design conditions. Although the design is presented using an investigation of Agent A with and without anti-PD-1 therapy as an illustrative example, the approach described is not specific to these agents and could be applied to other concurrent monotherapy and combination therapy studies with well-defined binary safety endpoints.

Collaborative Learning Communities With Medical Students as Teachers.

In recent years, peer-assisted learning has emerged as a new and effective medical education modality. Near-peer tutoring utilizes a senior student serving as an instructor to a junior student. In 2019, the University of California, Irvine, School of Medicine (UCISOM) implemented a near-peer tutoring model beginning with first-year anatomy and physiology curricula. Following a successful pilot program, UCISOM launched a full-fledged near-peer tutoring program in 2020 named Collaborative Learning Communities (CLC) with Medical Students as Teachers. The rollout of CLC occurred in phases. In 2020, second-year medical students led the program for first-year students; in 2021, an additional program was led by third-year medical students for second-year students; in 2022, the program expanded to third-year medical students led by fourth-year students. Each program serves the unique learning needs of each student class, utilizing evidence-based teaching practices while allowing the opportunity for mentorship, interclass connectedness, and refinement of the tutor's teaching skills. In this paper, we describe the creation of CLC, its goals, leadership and curricular structure, and its various benefits, challenges, and limitations.

Venetoclax with decitabine or azacitidine in the first-line treatment of acute myeloid leukemia.

Treatment paradigms for acute myeloid leukemia (AML) have evolved at a rapid pace in recent years. The combination of venetoclax with a hypomethylating agent prolonged survival in clinical trials when compared to hypomethylating agent monotherapy. However, little is known about the performance of venetoclax-based regimens outside of clinical trials, given conflicting safety and efficacy data. Even less is known about the impact of the hypomethylating agent backbone. In this study, we demonstrate that decitabine-venetoclax is associated with a significantly higher rate of grade three or higher thrombocytopenia, but lower rates of lymphocytopenia compared to azacitidine-venetoclax. There was no difference in response or survival across ELN 2017 cytogenetic risk categories in the overall cohort. Significantly more patients succumb to relapsed or refractory disease than death from any other cause. We demonstrated that a Charlson comorbidity index score threshold of seven identifies exceptionally high-risk patients, providing evidence for clinical use to reduce the risk of early treatment-related mortality. Lastly, we provide evidence that measurable residual disease negativity and an IDH mutation predict a significant survival benefit outside clinical trials. Taken together, these data illuminate the real-world performance of venetoclax and decitabine or azacitidine in the treatment of AML.

Reimagining Hair Science: A New Approach to Classify Curly Hair Phenotypes via New Quantitative Geometric and Structural Mechanical Parameters.

Hair is a natural polymeric composite primarily composed of tight macrobundles of keratin proteins, which are highly responsive to external stimuli, similarly to the hydrogels and other natural fibrous gel systems like collagen and fibrin.Hair and its appearance play a significant role in human society. As a highly complex biocomposite system, it has been traditionally challenging to characterize and thus develop personal care products. Over the last few decades, a significant societal paradigm shift occurred among those with curly hair, accepting the natural morphological shape of their curls and styling their hair according to its innate, distinct, and unique material properties, which has given rise to the development of new hair classification systems, beyond the traditional and highly limited race-based distinction (Caucasian, Mongolian, and African). L'Oréal developed a hair typing taxonomy based on quantitative geometric parameters among the four key patterns─straight, wavy, curly, and kinky, but it fails to capture the complex diversity of curly and kinky hair. Acclaimed celebrity hair stylist Andre Walker developed a classification system that is the existing gold standard for classifying curly and kinky hair, but it relies upon qualitative classification measures, making the system vague and ambiguous of phenotypic differences. The goal of this research is to use quantitative methods to identify new geometric parameters more representative of curly and kinky hair curl patterns, therefore providing more information on the kinds of personal care products that will resonate best with them and thus maximize desired appearance and health, and to correlate these new parameters with its mechanical properties. This was accomplished by identifying new geometric and mechanical parameters from several types of human hair samples.Geometric properties were measured using scanning electron microscopy (SEM), photogrammetry, and optical microscopy. Mechanical properties were measured under tensile extension using a texture analyzer (TA) and a dynamic mechanical analyzer (DMA), which bears similarity to the common act of brushing or combing. Both instruments measure force as a function of applied displacement, thus allowing the relationship between stress and applied stretch ratio to be measured as a hair strand uncurls and stretches to the point of fracture. From the resulting data, correlations were made between fiber geometry and mechanical performance. This data will be used to draw more conclusions on the contribution that fiber morphology has on hair fiber mechanics and will promote cultural inclusion among researchers and consumers possessing curly and kinky hair.

Isotonic design for phase I cancer clinical trials with late-onset toxicities.

This article addresses the problem of identifying the maximum tolerated dose (MTD) in Phase I dose-finding clinical trials with late-onset toxicities. The main design challenge is how best to adaptively allocate study participants to tolerable doses when the evaluation window for the toxicity endpoint is long relative to the accrual rate of new participants. We propose a new design framework based on order-restricted statistical inference that addresses this challenge in sequential dose assignments. We illustrate the proposed method on real data from a Phase I trial of bortezomib in lymphoma patients and apply it to a Phase I trial of radiotherapy in prostate cancer patients. We conduct extensive simulation studies to compare our design's operating characteristics to existing published methods. Overall, our proposed design demonstrates good performance relative to existing methods in allocating participants at and around the MTD during the study and accurately recommending the MTD at the study conclusion.

Quasi-partial order continual reassessment method: Applying toxicity scores to cancer dose-finding drug combination trials.

The primary endpoint of most dose-finding cancer trials is patient toxicity, and the primary goal is to identify the maximum tolerated dose (MTD), that is, the highest dose that falls below or within a pre-specified toxicity tolerability threshold. Conventionally, dose-finding methods have utilized a binary toxicity endpoint based on whether or not a patient experiences a dose limiting-toxicity (DLT). Improving upon this, in recent years several methods have been developed for modeling toxicity scores, a novel continuous endpoint designed to more precisely estimate patient toxicity burden. Separately, drug-combination trials have become increasingly prevalent, and due to added complexities regarding estimating 'true' dose ordering and potential for more complex patient toxicity profiles, provide an ideal setting which may benefit from the improved precision of toxicity scores. In this paper, we merge two frameworks based on the Continual Reassessment Method (CRM) - the Quasi-CRM and the Partial Order CRM (POCRM) - to propose a novel approach for modeling toxicity scores in a combination-trial setting. We demonstrate that utilizing toxicity scores has the potential to greatly improve correct dose-selection over a variety of trial scenarios. We further present a simple adaptation to the toxicity-score model to control for potential over-dosing issues such that it adheres to the conventional DLT definition and will, at worst, perform equivalently to that of the traditional binary DLT framework. We demonstrate that extending toxicity scores to the combination-trial setting offers potential for improvement over the conventional binary endpoint models.