Predict Treatment Resistance Research Articles

Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model

BackgroundTreatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP). MethodsLongitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures. ResultsThe prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56). ConclusionsTreatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.

Development and initial evaluation of a clinical prediction model for risk of treatment resistance in first-episode psychosis: Schizophrenia Prediction of Resistance to Treatment (SPIRIT).

A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication. To develop and evaluate a model that could predict the risk of TRS in routine clinical practice. We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model. We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723-0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism. We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.

A Comprehensive Review of Schizophrenia and Antipsychotic Metabolism as a Predictor of Treatment Response.

Some patients with schizophrenia fail to respond to standard antipsychotics and are considered treatment-resistant. In these cases, clozapine is the only antipsychotic with proven efficacy, but its use is complicated by severe adverse effects, complex monitoring requirements, and non-response. Variation within the CYP450 enzymes CYP1A2, CYP2D6, CYP3A4, and CYP2C19 has been linked to the differential metabolism of antipsychotics. Testing for CYP450 single nucleotide polymorphisms may be a useful predictor of treatment resistance and could inform pharmacogenetic recommendations to identify potential treatment non-responders. Nonetheless, it remains uncertain whether differential antipsychotic metabolism is directly related to treatment efficacy. This comprehensive narrative review endeavours to delve into the molecular and genetic basis of schizophrenia, and discuss the current treatments available. In particular, we aim to examine the aetiology of treatment resistance in schizophrenia through available literatureand discuss current challenges within the field.

Investigation of Hyaluronan Synthase 2 and CD44 immune reactivity as a biomarker to predict Progesterone-Resistant Endometrial Hyperplasia without atypia: A retrospective case-control study.

In our study, the effect of hyaluronan synthase 2 (HAS2) and CD44 immunoreactivity as a predictive biomarker in the prediction of progesterone-resistant endometrial hyperplasia (EH) cases without atypia was investigated. In this retrospective study, HAS2 and CD44 immunoreactivity in the endometrial tissues of 60 patients diagnosed with EH and treated with progesterone and 20 patients diagnosed with proliferative endometrium (PE) were evaluated. Eighty patients were divided into four groups. Group 1 (G1) (n = 20) = PE group, G2 (n = 20) = EH group without atypia, G3 (n = 20) = group with continued EH with treatment, G4 (n = 20) = EH with treatment without atypia. Intergroup evaluation was done with One-way ANOVA and posthoc tukey test. P < 0.05 values were considered statistically significant. The HAS2 immunoreactivity score of G2 and G3 was higher than G1 and G4. On the other hand, there was no difference between G1 and G4. When G2 and G3 were compared, HAS2 immunoreactivity scores were significantly increased in G3. When CD44 immunoreactivity was compared with G1, a significant increase was detected in G2, G3, and G4. However, CD44 immunoreactivity scores were similar in G2, G3, and G4. HAS2 immunoreactivity may be an immunohistochemical biomarker in predicting EH cases without atypia resistant to progesterone therapy. Since CD44 immunoreactivity is increased in all EH groups without atypia, it is not effective in predicting treatment resistance.

Clinical and laboratory findings characterizing the need for systemic corticosteroids and nonsteroidal systemic therapies and the predicted outcomes in cutaneous polyarteritis nodosa: a single-centre retrospective analysis.

Cutaneous polyarteritis nodosa (cPN) is a necrotizing arteritis of medium-sized vessels limited to the skin. Because of its rarity and the diversity of its clinical manifestations, there is no consensus treatment. Moreover, there are no established indicators that predict disease severity or its outcome. To investigate clinicolaboratory features that predict patients requiring systemic therapy, including corticosteroids, to control disease activity. Thirty-six patients with cPN who had not received systemic corticosteroids at their initial visit were retrospectively analysed by correlating the treatment and its response with clinicolaboratory findings. The major medications administered were antiplatelet agents [64% (23/36)], vasodilators [39% (14/36)] and prednisolone (PSL) [36% (13/36)]. In total, 23 patients achieved remission without PSL, 5 were managed with compression therapy alone or even observation and 18 received antiplatelet monotherapy or combined with vasodilator/dapsone whereas 13 required PSL. Of the 13 who required PSL, 10 achieved remission with PSL monotherapy or PSL and single/multiple medications and 3 with PSL and multiple drugs then failed to achieve remission and underwent limb amputation. There were more skin ulcers and an elevated peripheral white blood cell (WBC) count and erythrocyte sedimentation rate (ESR) before corticosteroid induction in patients requiring PSL. Three patients with treatment failure had a markedly elevated ESR (> 50 mm h-1). More than half of patients with cPN can achieve remission without corticosteroids. An elevated WBC, an elevated ESR and the presence of skin ulcers predict the need for PSL. A high ESR before corticosteroid induction predicts treatment resistance, even with PSL.

Abstract PO2-07-06: Multimodal learning predictor of HER2-positive breast cancer therapy response in the randomized PREDIX HER2 trial

Abstract Background: The PREDIX HER2 trial, compared six courses of docetaxel, trastuzumab, and pertuzumab (DTP) vs. trastuzumab emtansine (T-DM1) as neoadjuvant treatment for HER2-positive breast cancer (BC). Similar rates of pathologic complete response (pCR) were seen. Methods: Clinicopathological, shallow whole-genome sequencing (CUTseq, n=176), whole exome sequencing (WES, n=192), and RNA-sequencing (RNA-seq, n=187) data were generated using fresh-frozen baseline core biopsies. Potential tumor intrinsic resistance factors and microenvironment components were quantified by multi-omics analysis, including BC-specific somatic mutations and copy number alterations (CNA), COSMIC mutational signatures, CNA-based chromosomal instability signatures (CIN), subclone percentage, PAM50 subtype, GGI/PIK3CA score, HER2DX score, immune profiles (Danaher signature score, TIDE score and immune repertoires). We assessed the association of biomarkers with pCR in each treatment arm using logistic regression adjusting for hormone receptor (HR) status, and evaluated their predictive value by adding the interaction term (biomarker x treatment arm). In addition, a machine learning (ML) analysis was conducted from different classifiers, comprising unimodal ML-based models from clinical, RNA and DNA information, respectively. Model performance was assessed using the mean and standard deviation (mean ± std) of the area under receiver operator characteristic curve (AUC), positive predictive value (PPV) and negative predictive value (NPV) using a nested stratified cross-validation (CV) schema of 200 outer shuffle splits and 100 inner 5-fold CV to mitigate potential risk of overfitting. Results: In DTP arm, patients with higher ERBB2 copy ratio (ORadj=1.98, p=0.004) or mRNA (ORadj=3.08, p&amp;lt; 0.001) or HER2-enriched subtype (PAM50) (ORadj=1.78, p=0.02) had higher pCR rates, while ESR1 gene expression (ORadj=0.59, p=0.07) predicted treatment resistance despite adjustment for HR status. Conversely, response to T-DM1 was less likely to depend on ERBB2 profiles and only PAM50 HER2 enriched subtype (ORadj=1.53, p=0.1) showed higher pCR rate (52% vs. 25%) than other subtypes. Both ESR1 (ORadj=0.4, p=0.008) and PGR (ORadj=0.5, p=0.03) gene expression were independent predictors of T-DM1 resistance. Pre-treatment immune exclusion metrics could predict resistance to DTP (endothelial cell, ORadj=0.67, p=0.07) and T-DM1 (neutrophils, ORadj=0.54, p=0.02; mast cells, ORadj=0.57, p=0.02; cancer-associated fibroblasts, ORadj=0.67, p=0.09)), respectively. Predefined metrics such as PIK3CA signature score (ORadj =1.67, p=0.04) and Taxane response score (ORadj =1.64, p=0.03) were positively related to pCR in DTP arm. Genome instability, involving CIN CX2 signature (impaired homologous recombination) (ORadj=1.71, p=0.05), COSMIC signature6 (ORadj =1.53, p=0.07) and signature13 (ORadj =1.57, p=0.05), predicted benefit from DTP. The biomarker-treatment interaction tests were significant for HER2DX (pinteraction=0.004) and COSMIC signature15 (defective DNA mismatch repair) (pinteraction=0.007): lower HERDX score (ORadj =0.73, p=0.14) or higher COSMIC signature15 score (ORadj =1.51, p=0.1) could identify patients benefiting from T-DM1, while being resistant to DTP (HERDX: ORadj =1.46, p=0.13; signature15: ORadj =0.70, p=0.1). In the ML models, clinical information yielded an AUC=0.62±0.07, PPV=0.64±0.12 and NPV=0.64±0.06; for DNA data, AUC was equal to 0.70±0.08, PPV=0.72±0.09 and NPV=0.71±0.07; an adaptive boosting ensemble learning on RNA reported slightly increased pCR prediction performance (AUC=0.72±0.06, PPV=0.64±0.06, NPV=0.80±0.09). Conclusion: This study demonstrates that antibody–drug conjugates and standard treatment harbor strikingly distinctive biomarkers across tumor ecosystem. Further external validation and integrated ML model comprising all unimodal models are ongoing. Citation Format: Kang Wang, Yajing Zhu, Ioannis Zerdes, Emmanouil Sifakis, Georgios Manikis, Dimitrios Salgkamis, Nikolaos Tsiknakis, Luuk Harbers, Nicola Crosetto, Jonas Bergh, Alexios Matikas, Thomas Hatschek, Theodoros Foukakis. Multimodal learning predictor of HER2-positive breast cancer therapy response in the randomized PREDIX HER2 trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-06.

Clinical and Sociodemographic Correlations with Neurological Soft Signs in Hospitalized Patients with Schizophrenia: A Preliminary Longitudinal Study.

Schizophrenia is a severe, chronic neuropsychiatric disorder characterized by symptoms that profoundly impact behavior, cognition, perception, and emotions, leading to a reduced quality of life and physical impairment. Given the complexity of schizophrenia, there is a pressing need for clinical markers and tools to predict its course, enhance disease staging, facilitate early intervention, improve differential diagnosis, and tailor individualized treatment approaches. Previous studies focused on the relationship between neurological soft signs (NSS) and factors such as age, illness duration, and symptomatology, indicating NSS as state markers improving in parallel with psychotic symptom remission or predicting treatment resistance. However, there is a lack of consensus on NSS assessment tools, hindering routine clinical monitoring despite diagnostic and prognostic potential. The present longitudinal study involved 81 psychiatric inpatients diagnosed with schizophrenia. Patients were assessed at three time points: baseline, 1 month, and 6 months. The examination included the use of scales to evaluate psychotic and neurological symptoms, as well as the identification of adverse extrapyramidal reactions caused by neuroleptic treatment. The progression of NSS was correlated to both the symptomatology and the sociodemographic data of the patients. The main findings from the present investigation revealed a statistical correlation between NSS and psychopathological symptoms, especially with negative symptoms of schizophrenia. However, it is important to note that neuroleptic side effects only had a limited impact on NSS. Therefore, instead of being linked to extrapyramidal symptoms caused by neuroleptics, NSS appears to be more frequently related with symptoms of schizophrenia. Our findings provide further support for their strong association with the course of schizophrenia, independent of treatment side effects, thus emphasizing their potential as reliable assessment tools in both research and clinical settings.

Abstract 2391: Towards better prediction of treatment resistance: Directly modeling progression from multiple lesions

Abstract Late-stage cancers in the clinic present as a set of dispersed tumors, each with distinct growth kinetics. As most patients enrolled in trials of experimental anticancer therapeutics have late-stage malignancies, it is important to understand the impact of data analysis methodologies on the ability to detect treatment effect. In particular, we are interested in the potential risk posed by aggregating individual tumor measurements from multiple-lesion clinical data into a single tumor burden measurement, as this may cause a loss of information. In this work, we use quantitative modeling to assess how analyzing multiple lesions separately versus using an aggregated readout affects tumor response and detection of progression free survival and relapse. To assess the impact of data aggregation, we built a two-population clonal dynamics model consisting of sensitive and resistant tumor cells undergoing growth in the presence or absence of treatment. We performed parameter sweeps to assess the impact of individual model parameters on progression free survival and relapse. We then extended these sweeps to the multi-lesion situation, using nonlinear regression to compare parameter values of aggregated models and the parameter values of the individual models that comprised it. From our simulations, the most influential parameters in determining rebound are the growth rate of the resistant cells and the fraction of these cells, while growth rate of sensitive cells and growth rate inhibition affect effectiveness of treatment. In analyzing the multiple lesion model in different situations, we found that estimates for progression free survival were significantly more sensitive when the lesions were analyzed separately, rather than being aggregated. Our work suggests that aggregating the lesions into a single readout loses impact of individual lesion growth rate inhibitions, impairing early detection as the ability to detect fast growing, small, resistant lesions (despite overall tumor burden decreasing) is lost. Taken together, our findings suggest that data aggregation causes a loss of information. Relapse can be predicted at earlier times when using the multiple lesion model compared to an aggregated model. Citation Format: Samuel Protich, Madison Stoddard, Lin Yuan, Douglas White, Dean Bottino, Arijit Chakravarty. Towards better prediction of treatment resistance: Directly modeling progression from multiple lesions [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 2391.

Intestinal metabolites predict treatment resistance of patients with depression and anxiety

BackgroundThe impact of the gut microbiota on neuropsychiatric disorders has gained much attention in recent years; however, comprehensive data on the relationship between the gut microbiome and its metabolites and resistance to treatment for depression and anxiety is lacking. Here, we investigated intestinal metabolites in patients with depression and anxiety disorders, and their possible roles in treatment resistance.ResultsWe analyzed fecal metabolites and microbiomes in 34 participants with depression and anxiety disorders. Fecal samples were obtained three times for each participant during the treatment. Propensity score matching led us to analyze data from nine treatment responders and nine non-responders, and the results were validated in the residual sample sets. Using elastic net regression analysis, we identified several metabolites, including N-ε-acetyllysine; baseline levels of the former were low in responders (AUC = 0.86; 95% confidence interval, 0.69–1). In addition, fecal levels of N-ε-acetyllysine were negatively associated with the abundance of Odoribacter. N-ε-acetyllysine levels increased as symptoms improved with treatment.ConclusionFecal N-ε-acetyllysine levels before treatment may be a predictive biomarker of treatment-refractory depression and anxiety. Odoribacter may play a role in the homeostasis of intestinal L-lysine levels. More attention should be paid to the importance of L-lysine metabolism in those with depression and anxiety.

P348 Increased pre-treatment faecal calprotectin concentration predicts resistance to ustekinumab induction in ulcerative colitis

Abstract Background Rates of resistance to advanced therapy remain high in ulcerative colitis (UC). Pre-treatment predictors of poor outcomes would provide valuable information for treating clinicians so other therapies could be given or early treatment switches could be considered if response is inadequate. We investigated whether baseline, pre-treatment faecal calprotectin (FCP) can predict resistance to ustekinumab. Methods Clinical outcomes at week 8, including clinical remission, endoscopic healing and mucosal healing were determined in patients enrolled in the UNIFI phase 3 placebo-controlled trial program, investigating the efficacy of ustekinumab for moderate-to-severe UC (GSE206285), in whom FCP concentrations were available at baseline, prior to initiation of treatment. Clinical outcomes were defined in patients with FCP &amp;lt; 250mcg/g, 250 – 999mcg/g, 1000 – 1999mcg/g and &amp;gt;= 2000mcg/g. Chi-squared test was used to assess statistical significance. Area under the receiver operating characteristics curve (AUROC) analyses were used to determine the ability of faecal calprotectin level to predict treatment resistance. Analyses were performed using R 4.2.3 (Vienna, Austria) and GraphPad Prism 10.0.3 (Massachusetts, United States). Results Outcome data and linked faecal calprotectin levels were available for 340 patients. FCP values ranged from 15 – 25249mcg/g. The median FCP value was 1402mcg/g (interquartile range: 600.8 – 2772.0mcg/g). In general, all clinical outcome measures were progressively worse with increasing FCP concentrations. For the respective FCP cut-offs, 22.0%, 19.3%, 11.3% and 6.7% achieved clinical remission; 39.0%, 24.1%, 20.6% and 16.0% achieved endoscopic healing; and 29.3%, 16.9%, 15.5% and 7.6% achieved mucosal healing (Figure 1). ROC analyses revealed that pre-treatment FCP predicts resistance to ustekinumab therapy for all stated outcomes, including clinical remission (AUROC: 0.66, P=0.001), endoscopic healing (AUROC: 0.63, P=0.0013) and mucosal healing (AUROC: 0.65, P=0.0005). Conclusion Increasing concentrations of pre-treatment FCP predicts resistance to ustekinumab therapy in moderate-to-severe UC. Patients with FCP &amp;gt;= 2000mcg/g were significantly less likely than those with FCP &amp;lt;1000 mcg/g to achieve clinical remission or mucosal healing. Since neutrophils are the main producers of FCP, these data build on other emerging data indicating that excessive accumulation of mucosal neutrophils is associated with treatment resistance in IBD.

DOP31 Gene correlation network analyses reveal transcript modules and pathogenic mechanisms associated with resistance and response to ustekinumab therapy in Ulcerative Colitis

Abstract Background Precision medicine in ulcerative colitis (UC) remain elusive. The heterogeneity in patients’ mucosal transcriptomic profiles suggests that stratification based on molecular mechanisms might be possible, which may link to treatment response. We interrogated a dataset of ustekinumab-treated patients treated to determine transcript modules (TMs) in active UC, investigate pathogenic pathways and consider which cells might be driving ustekinumab resistance. Methods Pre-treatment mucosal transcriptomes and outcome data were available for 358 ustekinumab-treated patients enrolled in the UNIFI programme. Modules of co-expressed genes were defined with weighted gene correlation network analysis (WGCNA) and their functional roles and regulatory interactions inferred with Fisher tests. Multivariate regression was used to test the ability of clinical and transcriptomic data to predict treatment resistance. Further insights into the cellular and molecular features of ustekinumab resistance were gauged from the analysis of single cell data (Single Cell Portal accession SCP259). Analyses were performed in R 4.2.3 (Vienna, Austria). Results WGCNA generated 23 transcript modules (TMs) whose correlation with mucosal healing ranged from -0.26 to 0.25. The three TMs most negatively correlated with response comprised genes enriched in neutrophil degranulation, extracellular matrix activity and endoplasmic reticulum function. The modules correlating highest with response were enriched in genes involved in mitochondrial function. The constituents of the ustekinumab resistance-related modules are regulated by a hierarchy of transcription factors downstream of NFKB1, AHR, JUN and RELA. They are also densely connected by regulatory protein-protein interactions (Figure 1). A multivariate model including TM1 enrichment score and clinical variables predicts resistance with area under the curve 0.76 (95% confidence interval: 0.69–0.82). Notably, TM1 included TREM1 and OSM, which were previously associated with anti-TNF resistance. Surmising that cell populations co-expressing these genes might be key to ustekinumab resistance too, we identified TREM1+ OSM+ inflammatory monocytes (IMs). These showed higher expression of genes such as IL1B, IL8 and CXCL2 compared to other IMs, and increased activity in pathogenic processes including interleukin (IL)10, IL4 and IL13 signalling and G-protein-coupled receptor signalling. Conclusion Gene co-expression analysis identified transcripts and molecular mechanisms associated with response and predictive of resistance to ustekinumab. TREM1+ OSM+ IMs may be key to driving resistance, suggesting an important role for innate immunity. These findings are important for developing precision medicine approaches in UC.

A journey from omics to clinicomics in solid cancers: Success stories and challenges.

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Is System xc- a Suitable Target for Tumour Detection and Response Assessment with Imaging?

System xc- is upregulated in cancer cells and can be imaged using novel radiotracers, most commonly with (4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid (18F-FSPG). The aim of this review was to summarise the use of 18F-FSPG in humans, explore the benefits and limitations of 18F-FSPG, and assess the potential for further use of 18F-FSPG in cancer patients. To date, ten papers have described the use of 18F-FSPG in human cancers. These studies involved small numbers of patients (range 1-26) and assessed the use of 18F-FSPG as a general oncological diagnostic agent across different cancer types. These clinical trials were contrasting in their findings, limiting the scope of 18F-FSPG PET/CT as a purely diagnostic agent, primarily due to heterogeneity of 18F-FSPG retention both between cancer types and patients. Despite these limitations, a potential further application for 18F-FSPG is in the assessment of early treatment response and prediction of treatment resistance. Animal models of cancer have shown that changes in 18F-FSPG retention following effective therapy precede glycolytic changes, as indicated by 18F-FDG, and changes in tumour volume, as measured by CT. If these results could be replicated in human clinical trials, imaging with 18F-FSPG PET/CT would offer an exciting route towards addressing the currently unmet clinical needs of treatment resistance prediction and early imaging assessment of therapy response.

Large T cell clones expressing immune checkpoints increase during multiple myeloma evolution and predict treatment resistance

Tumor recognition by T cells is essential for antitumor immunity. A comprehensive characterization of T cell diversity may be key to understanding the success of immunomodulatory drugs and failure of PD-1 blockade in tumors such as multiple myeloma (MM). Here, we use single-cell RNA and T cell receptor sequencing to characterize bone marrow T cells from healthy adults (n = 4) and patients with precursor (n = 8) and full-blown MM (n = 10). Large T cell clones from patients with MM expressed multiple immune checkpoints, suggesting a potentially dysfunctional phenotype. Dual targeting of PD-1 + LAG3 or PD-1 + TIGIT partially restored their function in mice with MM. We identify phenotypic hallmarks of large intratumoral T cell clones, and demonstrate that the CD27− and CD27+ T cell ratio, measured by flow cytometry, may serve as a surrogate of clonal T cell expansions and an independent prognostic factor in 543 patients with MM treated with lenalidomide-based treatment combinations.

Development of a radiomics nomogram to predict the treatment resistance of Chinese MPO-AAV patients with lung involvement: a two-center study.

Previous studies from our group and other investigators have shown that lung involvement is one of the independent predictors for treatment resistance in patients with myeloperoxidase (MPO)-anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (MPO-AAV). However, it is unclear which image features of lung involvement can predict the therapeutic response in MPO-AAV patients, which is vital in decision-making for these patients. Our aim was to develop and validate a radiomics nomogram to predict treatment resistance of Chinese MPO-AAV patients based on low-dose multiple slices computed tomography (MSCT) of the involved lung with cohorts from two centers. A total of 151 MPO-AAV patients with lung involvement (MPO-AAV-LI) from two centers were enrolled. Two different models (Model 1: radiomics signature; Model 2: radiomics nomogram) were built based on the clinical and MSCT data to predict the treatment resistance of MPO-AAV with lung involvement in training and test cohorts. The performance of the models was assessed using the area under the curve (AUC). The better model was further validated. A nomogram was constructed and evaluated by DCA and calibration curves, which further tested in all enrolled data and compared with the other model. Model 2 had a higher predicting ability than Model 1 both in training (AUC: 0.948 vs. 0.824; p = 0.039) and test cohorts (AUC: 0.913 vs. 0.898; p = 0.043). As a better model, Model 2 obtained an excellent predictive performance (AUC: 0.929; 95% CI: 0.827-1.000) in the validation cohort. The DCA curve demonstrated that Model 2 was clinically feasible. The calibration curves of Model 2 closely aligned with the true treatment resistance rate in the training (p = 0.28) and test sets (p = 0.70). In addition, the predictive performance of Model 2 (AUC: 0.929; 95% CI: 0.875-0.964) was superior to Model 1 (AUC: 0.862; 95% CI: 0.796-0.913) and serum creatinine (AUC: 0.867; 95% CI: 0.802-0.917) in all patients (all p< 0.05). The radiomics nomogram (Model 2) is a useful, non-invasive tool for predicting the treatment resistance of MPO-AAV patients with lung involvement, which might aid in individualizing treatment decisions.

Evaluation of the Prognostic Value of Low-Frequency KRAS Mutation Detection in Circulating Tumor DNA of Patients with Metastatic Colorectal Cancer.

KRAS mutation in tumor tissue is a well-known predictor of resistance to the treatment of anti-EGFR antibodies in metastatic colorectal cancers (mCRC). However, the prognostic value of low-frequency plasma circulating tumor DNA (ctDNA) KRAS mutation in predicting treatment resistance in pretreated mCRC patients remains controversial. This study retrospectively reviewed the clinical course, including response to anti-EGFR and anti-VEGF therapies, and changes in serum tumor marker levels along with image studies in mCRC patients with <1.5% KRAS mutations detected in plasma ctDNA by next-generation sequencing (NGS) at a single center in Taiwan. We identified six pretreated mCRC patients with low-frequency KRAS G12V/G12D/G12S/G13D mutations (variant allele frequency 0.26~1.23%) in plasma ctDNA. Co-occurring low-frequency ctDNA mutations in APC, TP53, MAP2K1, KEAP1, or CTNNB1 were also detected. Although all six patients had treatment adjustments within one month after the ctDNA genetic test, image-evident tumor progression was noted in all patients within a median of 4 months afterwards. Re-challenge therapy with a combination of anti-EGFR, anti-VEGF, and FOLFIRI chemotherapy was found to be ineffective in a patient with 0.38% KRAS G12D mutation in baseline ctDNA. Our study suggests that the detection of low-frequency KRAS mutations in ctDNA could be used as a predictor of treatment response in mCRC patients.

Multi-factor dynamic analysis of ctDNA and CTC to aid the diagnostic prognosis of patients with metastatic breast cancer (MBC).

1038 Background: The monitoring of circulating tumor DNA (ctDNA) and circulating tumor cell (CTC) in patients with MBC predicts treatment resistance and prognosis. We previously reported that ESR1 alterations in ctDNA were associated with tumor tissue characteristics and CTCs which may predict metastasis and worse prognosis in MBC (2019 ASCO#1036, 2022 ASCO#1057). Furthermore, ctDNA can be used to evaluate tumor heterogeneity in MBC (2020 ASCO #1028). Here, we report a multi-factor analysis in ctDNA and CTC which may help to elucidate disease prognosis in MBC. Methods: This study included 391 MBC patients who received systemic treatment before 2022 under an IRB-approved clinical trial at NU Lurie Cancer Center. Whole blood samples (7.5ml/each) were collected from all patients at three time points including before treatment, three months, and six months after treatment for CTC enumeration using the CELLTRACKS system (Menarini). The corresponding ctDNA was analyzed by Guardant360 NGS evaluating 74-gene mutations. The maximum follow-up period was 380 months from first diagnosis. Causal Inference with Ensembel Learning was used for statistical analyses. Results: Of the 391 patients ctDNA analysis, TP53 mutations were found in 160 patients (TP53Mut, 40.92%), CDH1 mutations were found in 21 patients (CDH1Mut, 5.37%), Myc mutations were found in 53 patients (MycMut, 13.55%), and BRAF mutations were found in 35 patients (BRAFMut, 8.95%) inclusive of all timepoints. The corresponding ctDNA mutations were not detected in 231 patients for TP53 (TP53WT, 59.08%), in 370 patients for CDH1 (CDH1WT, 94.63%), in 338 patients for Myc (MycWT, 86.45%), or in 356 patients for BRAF (BRAFWT, 91.05%). There was shorter median overall survival (mOS) in the ctDNA mutated groups compared to the non-mutated groups: 88 months TP53Mut vs 184 months TP53WT, HR 1.91, P = 0.0002; 133 months CDH1WT vs 81 months CDH1Mut, HR 2.03, P = 0.02; 76 months Myc Mut vs 179 months MycWT , HR 3.24, P &lt; 0.0001, and 77 months BRAFMut vs 146 months BRAFWT HR 2.45, P = 0.007. Furthermore, ≥5 CTCs were found in 32.42% patients. These patient had a shorter mOS compared to patients had no CTC detected at any time point (70.1 months vs 131 months; P &lt; 0.001; HR 2.97). CTC-clusters were 50 times more likely to predict metastasis than single CTCs. We also found that greater than 5 CTC-clusters, clusters with 1-5, and no CTC-clusters were found in 12.55%, 11.25%, and 76.19% patients, respectively. Larger CTC-clusters had shorter mOS at 63 months, 121 months, and 163 months, respectively (P &lt; 0.0001). Conclusions: In this study, we identified multiple liquid biopsy factors including ctDNA and CTC-clusters at various time points, and found that these are associated with prognosis. The synergy of multiple ctDNA mutations and CTC-clusters during treatment may expand the predictive role of liquid biopsy for the monitoring of disease progression in patients MBC.

Abstract 1031: Specific gene alterations of HER2 positive single circulating tumor cell (CTC) compared to autologous leukocytes in metastatic breast cancer (MBC)

Abstract Introduction: Monitoring of CTC in MBC showed ability to predict treatment resistance and outcome. HER2 is one of the most important tumor markers which is associated with rapid cell division and the metastasis and prognosis of MBC in the clinic. More recently, we reported single CTC gene mutation is a key point to distinguish metastasis capability in MBC (2022 AACR and 2022 ASCO). Herein, we report specific gene alterations in HER2+ single CTC compared to autologous leukocytes in MBC. Methods: Whole blood sample (7.5ml/each) was collected from MBC patient before therapy. CTC enumeration was performed in FDA approved CellSearch™ System and then the single HER2+ CTC and leukocytes were isolated by using DEPArrayTM System (Menarini). The single cell DNA was isolated and then the initial library was prepared by using Ampli1™ Whole Genome Amplification, Ampli1™ QC Kit (Menarini) and AMPure XP kit (Beckman Coulter). The exome capture was performed by TruSeq DNA Exome kit (illumine). The sequencing was prepared by NextSeq 500 mid output V2.5 kit and was performed on the NextSeq 500 (Illumina). Results: We identified 107 CTCs including 55 HER2+ CTCs and 14 CTC-clusters. Autologous single HER2+ CTC (HER2+CK+CD45−, Group 1), HER2+CK−CD45+ leukocyte (Group 2) and HER2−CK−CD45+ leukocyte (Group 3) were isolated and sequenced respectively. The sequencing data was processed following the GATK pipeline and annotated using SnpEff. There were 486,119 counts (56.69%) for intron variants, 175,819 counts (20.51%) for intergenic variants, 70,334 counts (8.20%) for exon variants, 50,370 counts (5.87%) for downstream genes, 45,915 counts (5.36%) for upstream genes and others (3.37%) in HER2+ CTC. Meanwhile, there were 71,848 (8.76%) and 0 counts for exon variants found in Group 2 and Group 3 respectively. There were 79 gene variants ((SNP and Ins-Del) identified to have the highest impact effect (≥20) on HER2+ CTC chromosome, when there were 85 and 0 highest impact gene variants were identified in Group 2 and Group 3 respectively. The specific gene alterations in Group 1 compared to Group 2 includes CCNA2, FOLH1, BRD4, SAMHD1, CYP17A1, IDE, HPGDS and CTNNB1 Among the top 50 high impact gene variants, there were 26 genes alteration sites are same in both Group 1 and Group 2, including FECH, HDAC8, CYP11B2, TTN(6 sites), RAN, CD207, HK1, CASP1(2 sites), BRCA1, PIK3CG (3 sites), APEX1(4 sites), KIF11, SIRT5 (2 sites), XYLB and CHKA. Conclusion: Genomic characterization of HER2 positive single CTCs elucidated specific gene alterations associated with disease metastasis compared to autologous leukocytes in MBC. The newfound HER2+CK−CD45+ cells with gene alterations maybe a new kind of cancer transformed leukocytes which need further validation. Specific gene alterations will help to develop novel drugs aimed at the eradication of CTCs using molecularly driven therapies for disease metastasis. Citation Format: Qiang Zhang, Lorenzo Gerratana, Zhe Ji, Xinkun Wang, Paolo D’Amico, Seema Singhal, Youbin Zhang, Andrew A. Davis, Ami N. Shah, William Gradishar. Specific gene alterations of HER2 positive single circulating tumor cell (CTC) compared to autologous leukocytes in metastatic breast cancer (MBC) [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 1031.

PD38-04 METABOLIC HETEROGENEITY OF HUMAN PROSTATE CANCER BASED ON 18 F-PSMA-1007 AND 18 F-FDG PET/CT AND ITS CLINICOPATHOLOGIC CHARACTERISTICS

You have accessJournal of UrologyCME1 Apr 2023PD38-04 METABOLIC HETEROGENEITY OF HUMAN PROSTATE CANCER BASED ON 18F-PSMA-1007 AND 18F-FDG PET/CT AND ITS CLINICOPATHOLOGIC CHARACTERISTICS Sungun Bang, Hyun Ho Han, Dongwoo Kim, Mijin Yun, and Seung Hwan Lee Sungun BangSungun Bang More articles by this author , Hyun Ho HanHyun Ho Han More articles by this author , Dongwoo KimDongwoo Kim More articles by this author , Mijin YunMijin Yun More articles by this author , and Seung Hwan LeeSeung Hwan Lee More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003336.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: 18F-FDG Positron emission tomography/computed tomography (PET/CT) is limited in evaluating prostate cancer (PCa) due to low glycolysis in the primary tumor. Recently, PET/CT targeting the prostate specific membrane antigen (PSMA) has been useful for the diagnosis of biochemical recurrence and subsequent radioligand therapy (RLT). So far, there are limited reports on the value of PSMA PET/CT in detecting primary tumors of the prostate gland. We conducted this study to evaluate the values of 18F-PSMA-1007 and 18F-FDG PET/CT in the primary tumor detection and understand metabolic heterogeneity and related clinicopathologic characteristics at initial presentation. METHODS: This prospective study included 73 tumor regions from 42 patients who underwent both 18F-PSMA-1007 and 18F -FDG PET/CT and subsequent radical prostatectomy. SUVmax of the primary tumors on 18F-PSMA-1007 PET/CT were compared with the histological subtype and Gleason score (GS) by Kruskal-Wallis test with Bonferroni correction and with tumor size, serum PSA, and pathological stage (pStage). GS 6 and 7 were considered low and 8 to 10 were high. pStage I and II were considered early and the rest was advanced. Immunohistochemistry (IHC) was done for glucose transporter, PSMA, and androgen receptor (AR) expression. RESULTS: 18F-PSMA-1007 PET/CT detected all primary tumors with a detection sensitivity of 100% (73/73) whereas 18F-FDG PET/CT detected only 16% (12/73). When tumors were classified into low-GS acinar, high-GS acinar, and high-GS ductal types, SUVmax of 18F-PSMA-1007 uptake was significantly different among the three groups (SUVmax 8.9 in low-GS acinar, 14.7 in high-GS ductal, and 33.9 in high-GS acinar, all p values<0.05). In addition, high 18F-PSMA-1007 uptake was correlated with high serum PSA level, and advanced pStage. There were 12 primary lesions with both 18F-PSMA and 18F-FDG uptake demonstrating a flip-flop phenomenon between the two radiotracer uptake; the foci of increased 18F-FDG uptake showed low 18F-PSMA-1007 uptake and vice versa. All were advanced in pStage with low AR expression on IHC and most of them (9/12) were large ductal tumors. CONCLUSIONS: 18F-PSMA-1007 PET/CT was excellent in detecting primary tumors of all GS in the prostate gland. Some primary tumors showed a reciprocal pattern of 18F-PSMA-1007 and 18F-FDG uptake which were mostly large ductal type with low AR expression. Further studies are needed to see whether tumors with 18F-FDG uptake may predict treatment resistance to androgen deprivation therapy and PSMA RLT. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e994 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sungun Bang More articles by this author Hyun Ho Han More articles by this author Dongwoo Kim More articles by this author Mijin Yun More articles by this author Seung Hwan Lee More articles by this author Expand All Advertisement PDF downloadLoading ...

Predicting treatment resistance from first-episode psychosis using routinely collected clinical information.

Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia (TRS), but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for TRS of routinely collected, objective biomedical predictors at FEP onset, and to externally validate the model in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction Model fOr cloZApine tReaTment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels, and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index, and random glucose levels. The models were developed using data from two UK psychosis early intervention services (EIS) and externally validated in another UK EIS. Model performance was assessed via discrimination and calibration. We developed the models in 785 patients, and validated externally in 1,110 patients. Both models predicted clozapine use well at internal validation (MOZART: C 0.70; 95%CI 0.63,0.76; LASSO: 0.69; 95%CI 0.63,0.77). At external validation, discrimination performance reduced (MOZART: 0.63; 0.58,0.69; LASSO: 0.64; 0.58,0.69) but recovered after re-estimation of the lymphocyte predictor (C: 0.67; 0.62,0.73). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualisation tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.