Treatment Stratification Of Patients Research Articles

Phase II Trial of Pathology-based Tripartite Treatment Stratification for Patients with CNS Germ Cell Tumors: A Long-term Follow-up Study.

A previous Phase II clinical trial, conducted from 1995 to 2003, evaluated CNS germ cell tumors (GCTs) using a three-group treatment stratification based on histopathology. The primary objective of the study was to assess the long-term efficacy of standardized treatment regimens, while the secondary objective focused on identifying associated long-term complications. Total 228 patients were classified into three groups for treatment: germinoma (n=161), intermediate prognosis (n=38), and poor prognosis (n=28), excluding one mature teratoma case. Treatment involved stratified chemotherapy regimens and varied radiation doses/coverage. Clinical data was retrospectively analyzed at a median follow-up of 18.5 years. The treatment outcomes for germinoma, with or without syncytiotrophoblastic giant cell, were similar. The 10- and 20-year event-free survival rates for the germinoma, intermediate, and poor prognosis groups were 82/76/49% and 73/66/49%, respectively. Overall survival (OS) rates were 97/87/61% at 10 years and 92/70/53% at 20 years. Germinomas in the basal ganglia, treated without whole-brain radiation therapy (WBRT), frequently relapsed but were effectively managed with subsequent WBRT. Deaths in germinoma cases had varied causes, whereas deaths in the poor prognosis group were predominantly disease-related. Nineteen treatment-related complications were identified in 16 patients, with cumulative event rates of 1.9% at 10 years and 11.3% at 20 years. OS rates at 1 and 2 years post-relapse for tumors initially classified as germinoma, intermediate, and poor prognosis were 94/88/18% and 91/50/9%, respectively. Initial treatment intensity is crucial for managing non-germinomatous GCTs, while long-term follow-up for relapse and complications is imperative in germinomas. Irradiation extending beyond the immediate tumor site is essential for basal ganglia germinomas. Addressing relapse in non-germinomatous GCT remains a significant challenge.

Exploring the molecular landscape of cancer of unknown primary: A comparative analysis with other metastatic cancers.

Cancer of unknown primary (CUP) tumors are biologically very heterogeneous, which complicates stratification of patients for treatment. Consequently, these patients face limited treatment options and a poor prognosis. With this study, we aim to expand on the current knowledge of CUP biology by analyzing two cohorts: a well-characterized cohort of 44 CUP patients, and 213 metastatic patients with known primary. These cohorts were treated at the same institution and characterized by identical molecular assessments. Through comparative analysis of genomic and transcriptomic data, we found that CUP tumors were characterized by high expression of immune-related genes and pathways compared to other metastatic tumors. Moreover, CUP tumors uniformly demonstrated high levels of tumor-infiltrating leukocytes and circulating Tcells, indicating a strong immune response. Finally, the genetic landscape of CUP tumors resembled that of other metastatic cancers and demonstrated mutations in established cancer genes. In conclusion, CUP tumors possess a distinct immunophenotype that distinguishes them from other metastatic cancers. These results may suggest an immune response in CUP that facilitates metastatic tumor growth while limiting growth of the primary tumor.

Analyzing the risk factors for disease progression within 2years and histological transformation in patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone as first-line treatment: A 15-year follow-up of patients with advanced follicular lymphoma in JCOG0203.

Follicular lymphoma (FL) is an indolent lymphoma that becomes aggressive due to histological transformation (HT), leading to reduced survival. Patients with FL have different clinical courses and various treatment options. Some patients exhibit shorter survival and experience disease progression within 24months of diagnosis/treatment (POD24); the optimal treatment remains an unmet needs. Thus, identifying factors that predict shorter survival is essential to stratify treatment and prolong the survival of patients with FL. To analyze risk factors for POD24 and HT in patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) as first-line treatment, we performed this post-hoc analysis of patients with advanced indolent B-cell lymphoma in a randomized clinical trial wherein six cycles of R-CHOP were administered every 2-3weeks. The primary analysis showed no differences in outcomes, which enabled the analysis of 248 patients with FL, assigned to two arms. All histopathological specimens from the 300 enrolled patients were reviewed by three expert hematopathologists. Multivariable analysis implicated Follicular Lymphoma International Prognostic Index (FLIPI) intermediate (odds ratio [OR] 2.531, 95% confidence interval [CI] 0.676-9.466) and high- (OR 2.236, 95% CI 0.160-31.226) risks, B symptoms (OR 2.091, 95% CI 0.747-5.851), and grade 3A (G3A) (OR 1.833, 95% CI 0.634-5.299) as risk factors for POD24. Furthermore, multivariable analysis through a median follow-up of 15.9years implicated G3A (OR 2.628, 95% CI 0.806-8.575) and high-risk FLIPI (OR 4.401, 95% CI 0.186-104.377) as risk factors for HT. However, an analysis limited to the first 10years revealed that the prognostic factors elucidated from the longer-term analysis had a greater impact on HT. G3A and high-risk FLIPI may independently predict POD24 and HT, thereby informing treatment stratification of patients with untreated advanced-stage FL in future trials, particularly to address the unmet needs of patients with POD24.

Ex-vivo drug screening of surgically resected glioma stem cells to replace murine avatars and provide personalise cancer therapy for glioblastoma patients

With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas.

P53 and TLR4 expression are prognostic markers informing progression free survival of advanced stage high grade serous ovarian cancer

ObjectiveNew prognostic biomarkers, and bio-signatures, are urgently needed to facilitate a precision medicine-based approach to more effectively treat patients with high-grade serous ovarian cancer (HGSC). In this study, we analysed the expression patterns of a series of candidate protein biomarkers. MethodsThe panel of markers which included MyD88, TLR4, MAD2, PR, OR, WT1, p53, p16, CD10 and Ki67 was assessed using immunohistochemistry in a tissue microarray (TMA) cohort of n = 80 patients, composed of stage 3–4 HGSCs. Each marker was analysed for their potential to predict both overall survival (OS) and progression-free survival (PFS). ResultsTLR4 and p53 were found to be individually predictive of poorer PFS (Log Rank, p = 0.017, p = 0.030 respectively). Cox regression analysis also identified high p53 and TLR4 expression as prognostic factors for reduced PFS (p53; HR=1.785, CI=1.036–3.074, p = 0.037 and TLR4; HR=2.175, CI=1.112–4.253, p = 0.023). Multivariate forward conditional Cox regression analysis, examining all markers, identified a combined signature composed of p53 and TLR4 as prognostic for reduced PFS (p = 0.023). ConclusionCombined p53 and TLR4 marker assessment may help to aid treatment stratification for patients diagnosed with advanced-stage HGSC.

ACTB may serve as a predictive marker for the efficacy of lenvatinib in patients with HBV-related early-stage hepatocellular carcinoma following partial hepatectomy: a retrospective cohort study.

The lack of effective biomarkers for the treatment of postoperative recurrence in hepatocellular carcinoma (HCC) persists despite lenvatinib therapy. This study aims to identify beta-actin (ACTB) as a predictive biomarker for lenvatinib that can facilitate individualized treatment for HCC. This retrospective study included a subset of patients with HCC who underwent partial hepatectomy, with some receiving postoperative lenvatinib treatment and others not receiving lenvatinib treatment. A propensity score matching (PSM) analysis of patients who underwent treatment with or without lenvatinib following HCC partial hepatectomy was performed. Immunohistochemistry was employed to determine the levels of ACTB expression in HCC samples obtained from matched patients (n=225) enrolled in this study. The X-Tile was employed to determine the optimal cut-off point of ACTB levels for predicting time to recurrence (TTR). To assess the correlation between ACTB levels and lenvatinib efficacy, a subgroup analysis of TTR was conducted. A Cox regression model with an interaction term was utilized to assess the predictive significance of the model. Subsequently, a nomogram was developed and its discriminative ability and predictive accuracy were assessed using the concordance index (C-index) and calibration curve. For the investigation of the ACTB expression, HCC and para-tumoral normal tissues were employed. The patient-derived xenograft (PDX) model was utilized to validate the correlation between ACTB levels and lenvatinib responsiveness. After PSM, a total of 76 patients who underwent postoperative lenvatinib treatment were included in the analysis, with a median TTR of 24.35 months. Early-stage HCC patients with lower levels of ACTB exhibited a more favorable response to lenvatinib therapy compared to those with higher levels. The reduced expression of ACTB was indicative of the benefits of lenvatinib, as opposed to higher levels {hazard ratio (HR) =0.243 [95% confidence interval (CI): 0.096-0.619], P<0.001, P value for interaction =0.014}. In approximately 81.8% of cases involving HCC patients, there was an observed increase in the expression of ACTB. Multivariate analysis of the lenvatinib cohort revealed Child-Pugh [HR =5.416 (95% CI: 1.390-21.104), P=0.015], Barcelona Clinic Liver Cancer (BCLC) stage [HR =2.508 (95% CI: 1.116-5.639), P=0.026], and ACTB [HR =5.879 (95% CI: 2.424-14.259), P<0.001] score as independent factors for TTR, and all were included in the nomogram. The survival probability based on the calibration curve showed that the prediction of the nomogram was in good agreement with the actual observation. The C-index of the nomogram for predicting survival was 0.76 (95% CI: 0.71-0.84). Moreover, the PDXs derived from tumors exhibiting low levels of ACTB expression demonstrated a heightened sensitivity to lenvatinib treatment. In patients with tumors treated with lenvatinib, low ACTB expression can predict a lower risk of recurrence. The validation of this potential biomarker in independent cohorts is necessary prior to its implementation for precision treatment stratification in patients undergoing partial hepatectomy for early-stage HCC.

Validation of the GRACE 3.0 score and redefinition of the risk threshold for early invasive treatment in non-ST-segment elevation acute coronary syndromes: a modelling study from five countries

Abstract Background Clinical use of the GRACE scoring system is recommended across international guidelines to guide early treatment stratification in patients with non-ST-segment elevation acute coronary syndromes (NSTE-ACS). Recently, the machine learning-based GRACE 3.0 score was derived from patients with NSTE-ACS undergoing contemporary treatment approaches. External validation studies and the reassessment of clinical risk categories are lacking. Purpose We aimed to evaluate the predictive performance of the GRACE 3.0 score and to explore clinically meaningful risk groups in contemporary patients with NSTE-ACS. Methods We studied the GRACE 3.0 score in 8070 patients with NSTE-ACS in contemporary ACS cohorts from Denmark (VERDICT, n=2147), Germany (Heidelberg-ACS, n=2034), Italy and Spain (CORALYS, n=1650), and Switzerland (SPUM-ACS, n=2239). Heterogeneity in the treatment effect of very early invasive management (within 12 hours) or standard invasive management (within 48 to 72 hours) on the primary composite outcome of all-cause death, nonfatal recurrent myocardial infarction, hospital admission for refractory myocardial ischemia, or hospital admission for heart failure in relation to baseline mortality risk was assessed in 2147 patients enrolled in the VERDICT trial who were randomized to different timing of invasive treatment. Results The GRACE 3.0 score showed excellent discriminatory properties with an area under the receiver operating characteristic curve (AUC) for in-hospital death of 0.89 (95% CI, 0.85–0.93) in the total study cohort. Similar results were observed in Denmark (AUC 0.93, 95% CI, 0.88–0.98), Germany (AUC 0.90, 95% CI, 0.85–0.94), and Italy and Spain (AUC 0.85, 95% CI, 0.77–0.94), and Switzerland (AUC 0.93, 95% CI, 0.86–1.00). Early invasive treatment reduced the composite endpoint (absolute risk reduction: 0.12, 95% CI, 0.03–0.21, P=0.011) in patients at high risk (≥1.7%), but not in those with lower risk profiles (P interaction=0.033). Based on this new high-risk threshold, GRACE 3.0 stratified 475 (42.2%) more patients into the high-risk group, as compared to traditional GRACE risk categories (P&amp;lt;0.001). Conclusion GRACE 3.0 shows unprecedented predictive performance in patients with NSTE-ACS. In the context of a comprehensive clinical evaluation, GRACE 3.0 can support clinical decision making for the timing of invasive treatment. The high-risk group of patients with NSTE-ACS who benefit from early invasive treatment is substantially larger than previously described.ROC curve and risk groupsRestratfication towards high risk group

DNA mismatch repair deficient cancer - Emerging biomarkers of resistance to immune checkpoint inhibition.

The DNA mismatch repair pathway is involved in the identification, excision, and repair of base-base mismatches and indel loops in the genome. Mismatch repair deficiency occurs in approximately 20% of all cancers and results in a type of DNA damage called microsatellite instability. In 2017, the immune checkpoint inhibitor, Pembrolizumab, an anti-PD-1 therapy, was approved for use in all unresectable or metastatic tumours that were mismatch repair deficient or had high microsatellite instability regardless of tissue origin. This landmark approval was the first time a drug had been approved in a site agnostic way, but accumulating data has revealed that up to 50% of mismatch repair deficient tumours are refractory to treatment and there is a huge amount of variability in the therapeutic benefit amongst responders. Several mechanisms of resistance to immune checkpoint blockade for mismatch repair deficient cancers have been identified but our understanding of what is driving resistance in a proportion of patients remains lacking. In this review article, we discuss the emerging mechanisms of resistance which may enable optimal stratification of patients for treatment with immune checkpoint inhibitors in the future.

An Update on the Role of MRI in Treatment Stratification of Patients with Cervical Cancer.

Cervical cancer is the fourth most common cancer in women worldwide and the most common gynaecological malignancy. The FIGO staging system is the most commonly utilised classification system for cervical cancer worldwide. Prior to the most recent update in the FIGO staging in 2018, the staging was dependent upon clinical assessment alone. Concordance between the surgical and clinical FIGO staging decreases rapidly as the tumour becomes more advanced. MRI now plays a central role in patients diagnosed with cervical cancer and enables accurate staging, which is essential to determining the most appropriate treatment. MRI is the best imaging option for the assessment of tumour size, location, and parametrial and sidewall invasion. Notably, the presence of parametrial invasion precludes surgical options, and the patient will be triaged to chemoradiotherapy. As imaging is intrinsic to the new 2018 FIGO staging system, nodal metastases have been included within the classification as stage IIIC disease. The presence of lymph node metastases within the pelvis or abdomen is associated with a poorer prognosis, which previously could not be included in the staging classification as these could not be reliably detected on clinical examination. MRI findings corresponding to the 2018 revised FIGO staging of cervical cancers and their impact on treatment selection will be described.

Serum immune checkpoint profiling identifies soluble CD40 as a biomarker for pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) responds poorly to systemic treatment, including new immunotherapeutic approaches. Biomarkers are urgently needed for early disease detection, patient stratification for treatment, and response prediction. The role of soluble CD40 (sCD40) is unknown in PDAC. In this study, we performed a quantitative multiplex analysis of 17 immune checkpoint proteins in serum samples from patients with various stages of PDAC in a discovery study (n = 107) and analyzed sCD40 by ELISA in a validation study (n = 317). Youden’s J statistic was used for diagnostic cut-off optimization. A Cox proportional hazards regression model was applied in an empiric approach for prognostic threshold optimization. Kaplan–Meier estimator and multivariable Cox regression analyses were used for survival analysis. sCD40 was significantly increased in the serum of patients with PDAC compared to healthy cohorts and patients with IPMN. In the validation cohort, the area under the receiver operating characteristic (ROC) c-statistic was 0.8, and combining sCD40 with CA19-9 yielded a c-statistic of 0.95. sCD40 levels were independent of the tumor stage. However, patients who received neoadjuvant chemotherapy had significantly lower sCD40 levels than those who underwent upfront surgery. Patients with a sCD40 level above the empirical threshold of 0.83 ng/ml had a significantly reduced overall survival with a hazard ratio of 1.4. This observation was pronounced in patients after neoadjuvant chemotherapy. Collectively, soluble CD40 may be considered as both a diagnostic and prognostic non-invasive biomarker in PDAC.

DEVELOPMENT OF GLIEXP; A HIGH-THROUGHPUT EX VIVO DRUG SCREENING PLATFORM FOR GLIOBLASTOMA

Abstract AIMS With diminishing returns and common clinical failure rates from traditional preclinical and animal-based drug discovery strategies, emphasis is being placed on alternative drug discovery platforms, such as ex vivo approaches. Such approaches represent a departure from both preclinical animal-based models and more traditional clinical-based strategies and aim to address intra/inter-patient variability at an earlier stage of drug discovery. Additionally, they could also offer precise treatment stratification for patients within a week of surgery to direct a tailored treatment course. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy- resistant glioma stem cell (GSC) niches, and where historic preclinical models have failed to generate new therapies leading to 50-year stagnant dire survival rates of around 15 months post-diagnosis. METHOD We have developed and optimized a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. RESULTS As a proof-of-concept for GliExP, we have optimized standard-of-care chemoradiotherapy responses which can correctly predict MGMT status based on Temozolomide sensitivity. We have screened 30+ small molecule therapeutics and preclinical compounds against 18 different patient’s tumours, including multi-region spatial het- erogeneity sampling from several individual tumours. Drug responses are further correlated to transcriptomic and genetic profiles. CONCLUSIONS Our data provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with standard-of-care therapies as an important preclinical model to triage experimental therapeutics for clinical translation, delivering rapid identification of effective treatment strategies for individual gliomas.

Ex-vivo drug screening of surgically resected glioma stem cells to replace murine avatars and provide personalise cancer therapy for glioblastoma patients.

With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas.

Beyond ARIA: Will e-diaries replace retrospective questionnaires in measuring the severity of allergic rhinitis in clinical research and daily practice?

Retrospective questionnaires are used since decades to assess the severity and/or control of allergic diseases. Applications on smartphones have recently facilitated the use of prospective clinical diaries, based on questionnaires filled every day by the patient. Once limited to clinical trials, these e-diaries, based on validated disease control scores and visual analogue scales, permit a quantitative day-by-day measure free of recall bias. Given the advantages of this procedure, its use could be extended to the daily clinical practice. E-diaries may facilitate (1) a more precise identification of the culprit allergen in the diagnostic work-up of poly-sensitized patients, (2) the stratification of patients for treatment, (3) the follow-up of the patients under treatment for optimized shared decision-making, and (4) a careful assessment of preventive therapies. While a few apps are being used in scientific studies, consensus on their use in daily practice should be reached and guidelines for specialists should be elaborated by scientific associations.

Development and characterization of an ETV1 rabbit monoclonal antibody for the immunohistochemical detection of ETV1 expression in cancer tissue specimens

BackgroundAberrant ETV1 overexpression arising from gene rearrangements or mutations occur frequently in prostate cancer, round cell sarcomas, gastrointestinal stromal tumors, gliomas, and other malignancies. The absence of specific monoclonal antibodies (mAb) has limited its detection and our understanding of its oncogenic function. MethodsAn ETV1 specific rabbit mAb (29E4) was raised using an immunogenic peptide. Key residues essential for its binding were probed by ELISA and its binding kinetics were measured by surface plasmon resonance imaging (SPRi). Its selective binding to ETV1 was assessed by immunoblots and immunofluorescence assays (IFA), and by both single and double-immuno-histochemistry (IHC) assays on prostate cancer tissue specimens. ResultsImmunoblot results showed that the mAb is highly specific and lacked cross-reactivity with other ETS factors. A minimal epitope with two phenylalanine residues at its core was found to be required for effective mAb binding. SPRi measurements revealed an equilibrium dissociation constant in the picomolar range, confirming its high affinity. ETV1 (+) tumors were detected in prostate cancer tissue microarray cases evaluated. IHC staining of whole-mounted sections revealed glands with a mosaic staining pattern of cells that are partly ETV1 (+) and interspersed with ETV1 (−) cells. Duplex IHC, using ETV1 and ERG mAbs, detected collision tumors containing glands with distinct ETV1 (+) and ERG (+) cells. ConclusionsThe selective detection of ETV1 by the 29E4 mAb in immunoblots, IFA, and IHC assays using human prostate tissue specimens reveals a potential utility for the diagnosis, the prognosis of prostate adenocarcinoma and other cancers, and the stratification of patients for treatment by ETV1 inhibitors.

Abstract 5300: Mass spectrometry-based protein biomarker analysis in chemoimmunotherapy combinations identifies unique immune signatures in pancreatic cancer

Abstract Although the combination of chemotherapy with immunotherapy has led to significant improvements in the treatment of some solid tumors, metastatic pancreatic ductal adenocarcinoma (mPDAC) prognosis has remained largely unaffected by such approaches. Recently, PRINCE, a randomized phase 2 clinical study, reported significantly improved 1-year overall survival (OS) for mPDAC patients treated with nivolumab and chemotherapy (nivo/chemo) compared to historical control but not for sotigalimab and chemotherapy (sotiga/chemo) or a combination of the three. Interestingly, the study identified potential improvement to treatment outcome with patient stratification. Here, we report an unbiased mass spectrometry (MS)-based proteomics profiling of a subset of plasma samples from the PRINCE trial (n = 211, 62 individuals from nivo/chemo and sotiga/chemo longitudinal samples). Briefly, plasma samples were depleted, digested to tryptic peptides, measured by MS/MS and quantified using Spectronaut (Biognosys). Data was investigated for both protein and peptide biomarker with an emphasis on baseline biomarkers associated with clinical outcomes. Plasma profiling identified 1,662 proteins and 17,451 modified peptides across the cohorts. First, we developed a model that identified the pharmacodynamic effects of treatment in individual patients. In accordance with the PRINCE study and among the major model contributors, sotiga/chemo increased proteins associated with innate immunity and chemokines (including CCL15) while proteins aiding in T cell activation and immune cell migration increased with nivo/chemo (including CXCL7 and CD115). Second, we looked for markers in the pretreatment plasma samples that could predict OS. Overall, we found 25 predictive markers (p &amp;lt; 0.05), with only six shared among the two arms, including attractin and CD58. Among the seven predictive biomarkers specific to nivo/chemo, we found MEGF10 and GALNT1, which are suggested to play a role in neoantigen generation. For sotiga/chemo, we found 12 predictive biomarkers including IGF2, CD304, and periostin (known to support immune responses). Third, we expanded our predictive biomarker search to the identified peptides, an analysis that is currently only possible using an unbiased mass spectrometry-based approach. Using such an approach we identified predictive peptides, likely cleavage products, as well as predictive post-translational modifications. Herein we demonstrate the value of both an unbiased approach, as well as the use of peptide level data for novel biomarker identification. We identify numerous proteins and peptides that have the potential to be used for better patient treatment stratification in the case of mPDAC and immunotherapy. Citation Format: Nigel Beaton, Marco Tognetti, Kamil Sklodowski, Roland Bruderer, Lukas Reiter. Mass spectrometry-based protein biomarker analysis in chemoimmunotherapy combinations identifies unique immune signatures in pancreatic cancer. [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 5300.

From Generic RNA-Based MRD Assessment Towards Patient-Specific Genomic (DNA-based) MRD in AML and Other Non-B Cell Acute Leukemia

Pediatric acute myeloid (AML) is considered as a heterogeneous disease affecting 15% of children with acute leukemia. Outcome is still unsatisfactory with an overall survival around 70-80% and a relapse rate of 30-40% after primary treatment. The most important factors determining clinical outcome are genetic aberrations, and early response to treatment. Many studies have demonstrated the prognostic significance of minimal residual disease (MRD) monitoring. Although MRD monitoring is now an integral part in the management (including MRD-guided stratification) of Acute Lymphoblastic Leukemia (ALL) in children (generally done by ASO-qPCR on tumor specific IG/TCR targets), its clinical utility in childhood non-lymphoblastic leukemia (such as AML) is currently unknown/not used. Contemporary approaches to MRD detection in AML rely on PCR amplification of usually fixed genetic abnormalities (MRD-PCR) or on flowcytometric profiling of leukemia-associated immunophenotypes (LAIP). Molecular MRD detection is in general more sensitive than flow-based MRD, but the applicability is lower since recurrent mutational targets (e.g. fusion genes) using conventional technology, are present in only 40-50% of pediatric AML patients. This RNA-based MRD is common practice for CBFb-MYH11, RUNX1-RUNXT1, KMT2A-AFF1, -MLLT1, -MLLT3, and NPM1 exon 12 mutations and standard curves necessary for MRD quantification are available for all mentioned genes. Since the introduction of Whole Transcriptome Sequencing (WTS; RNAseq) as standard diagnostics in our laboratory, we identified various additional fusion genes in AML (increasing available molecular targets up to 75%), but also in other non-B cell malignancies such as SCLL, CEL, HES and BPDCN. By designing patient specific primers/probe sets surrounding the breakpoint sequences, we were able to generate 36 RNA-based MRD assays (including FOP-FGFR1, RANBP2-ALK, DEK-NUP214, ETV6-PDGFRB, KMT2A-AFDN) and monitor MRD during/after therapy. Although RNA-based MRD approach allows additional molecular monitoring in these patients, this type of MRD displays several disadvantages: sensitivity of a certain MRD assay can be low due to low expression levels of the fusion gene or low RNA yield and/or quality. Furthermore, quantification can be hampered by lack of available standard curves. Therefore DNA-based MRD is favorable over RNA-based MRD and Whole Exome Sequencing (WES) or Whole Genomic Sequencing (WGS) can identify the genomic breakpoint sequence in respectively genomic exons or introns. In addition, other genomic aberrations (such as insertions, deletions and point mutations) can be identified by these techniques and used for development of DNA-based MRD. By using this approach we were able to generate 17 additional DNA-based MRD assays (including FOP-FGFR1, RANBP2-ALK, RUNX1-CBFA2T3 NPM1-MLF1 UBTF-TD, p53 deletion), all with very high sensitivity (e.g. around 10-4 to 5x10-5) and patient-derived standard curves to quantify MRD. Figure 1 shows an example for DNA-based MRD monitoring of 3 AML patients harboring the recently described poor-outcome UBTF-TD aberration showing no/slow decrease of MRD in all patients. In conclusion, we show that fusion gene detection by WTS, preferably in combination with mutation analysis by WES/WGS, is providing comprehensive diagnostic results to improve the development of highly sensitive patient specific MRD targets to allow better molecular monitoring (and prediction of relapse), eventually leading to treatment stratification of patients in the future. We will present an update of our results and interesting cases at the upcoming conference. Figure 1: By WES, 3 patients with tandem duplications (TD) in exon 13 of the UBTF gene were identified, and patient specific primers and probes were designed for MRD on follow-up DNA samples. MRD was quantified by using a standard curve derived from serial dilutions from the diagnostic DNA. Quantitative range, sensitivity and background were determined according to the ESG-MRD-ALL guidelines. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Abstract 3923: Ubiquitin ligases implicated as predictive biomarkers for poor outcome to immunotherapy in melanoma patients

Abstract Background From a medical standpoint, melanoma is considered the most serious form of skin cancer. Immunotherapy treatment regimes frequently target the PD1 pathway and can be highly effective. Despite successes some patients remain unresponsive and a better understanding of what underlies these non-responders could enable better patient treatment stratification and an overall higher positive response rate. Here using unbiased mass spectrometry (LC-MS) proteomics profiling, we sought to identify protein biomarker signatures in patients with metastatic melanoma that correlate with the patients’ intervention responses. Via this discovery-based approach we identify a panel of protein biomarkers that correlate with an improved treatment response and could serve as a panel for patient stratification prior to commencement of immunotherapy regimes. Methods Formalin-fixed paraffin-embedded (FFPE) tumor samples were used for unbiased quantification of proteins using data-independent acquisition (DIA) LC-MS technology and Biognosys’ Spectronaut software. Patient samples were analyzed at the start of treatment (PD1 inhibitor) and classified as responders (n = 9) or non-responders (n = 15) at 3 months post-treatment. Significant protein expression changes between the two groups was determined by t-test (p &amp;lt; 0.01, log2 fold-change &amp;gt; 0.58) and further machine learning-assisted analysis was conducted. Additionally, patient’s tumor mutational burden (TMB) was analyzed. Results Analysis of the FFPE samples from anti-PD1-treated metastatic melanoma patients resulted in the identification and quantification of more than 9,000 proteins in total with an average of nearly 8,000 proteins per sample. Stratification of the patients into responders and non-responders led to the identification of 103 proteins that are significantly up- or down-regulated between the groups. Interestingly, proteins associated with ubiquitination pathways were significantly enriched among the identified regulated proteins with 7 proteins having significantly lower abundance in responders when compared to non-responders including various members of the ubiquitin ligase family. Further, a proteomic signature was identified that characterizes the responder and non-responder groups. Conclusions In this study we demonstrate the power of unbiased proteomic profiling for the characterization of melanoma patients and their responses to PD1-targeted immunotherapy. We identify a set of biomarkers associated with ubiquitination pathways that correlate with treatment outcome although these findings need to further be confirmed in orthogonal cohorts. Citation Format: Nigel Beaton, Tobias Treiber, Anna Juncker-Jensen, Xin-Xing Tan, Julia Martinez Gomez, Mitchell Levesque, Jakob Vowinkel. Ubiquitin ligases implicated as predictive biomarkers for poor outcome to immunotherapy in melanoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3923.

The potential of precision psychiatry: what is in reach?

Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.

Quantitative comparison of PD-L1 IHC assays against NIST standard reference material 1934

Companion diagnostic immunohistochemistry (IHC) tests are developed and performed without incorporating the tools and principles of laboratory metrology. Basic analytic assay parameters such as lower limit of detection (LOD) and dynamic range are unknown to both assay developers and end users. We solved this problem by developing completely new tools for IHC—calibrators with units of measure traceable to National Institute of Standards & Technology (NIST) Standard Reference Material (SRM) 1934. In this study, we demonstrate the clinical impact and opportunity for incorporating these changes into PD-L1 testing. Forty-one laboratories in North America and Europe were surveyed with newly-developed PD-L1 calibrators. The survey sampled a broad representation of commercial and laboratory-developed tests (LDTs). Using the PD-L1 calibrators, we quantified analytic test parameters that were previously only inferred indirectly after large clinical studies. The data show that the four FDA-cleared PD-L1 assays represent three different levels of analytic sensitivity. The new analytic sensitivity data explain why some patients' tissue samples were positive by one assay and negative by another. The outcome depends on the assay's lower LOD. Also, why previous attempts to harmonize certain PD-L1 assays were unsuccessful; the assays' dynamic ranges were too disparate and did not overlap. PD-L1 assay calibration also clarifies the exact performance characteristics of LDTs relative to FDA-cleared commercial assays. Some LDTs' analytic response curves are indistinguishable from their predicate FDA-cleared assay. IHC assay calibration represents an important transition for companion diagnostic testing. The new tools will improve patient treatment stratification, test harmonization, and foster accuracy as tests transition from clinical trials to broad clinical use.

Pre-clinical and clinical studies on the role of RBM3 in muscle-invasive bladder cancer: longitudinal expression, transcriptome-level effects and modulation of chemosensitivity

BackgroundThe response to neoadjuvant cisplatin-based chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is impaired in up to 50% of patients due to chemoresistance, with no predictive biomarkers in clinical use. The proto-oncogene RNA-binding motif protein 3 (RBM3) has emerged as a putative modulator of chemotherapy response in several solid tumours but has a hitherto unrecognized role in MIBC.MethodsRBM3 protein expression level in tumour cells was assessed via immunohistochemistry in paired transurethral resection of the bladder (TURB) specimens, cystectomy specimens and lymph node metastases from a consecutive cohort of 145 patients, 65 of whom were treated with NAC. Kaplan-Meier and Cox regression analyses were applied to estimate the impact of RBM3 expression on time to recurrence (TTR), cancer-specific survival (CSS), and overall survival (OS) in strata according to NAC treatment. The effect of siRNA-mediated silencing of RBM3 on chemosensitivity was examined in RT4 and T24 human bladder carcinoma cells in vitro. Cellular functions of RBM3 were assessed using RNA-sequencing and gene ontology analysis, followed by investigation of cell cycle distribution using flow cytometry.ResultsRBM3 protein expression was significantly higher in TURB compared to cystectomy specimens but showed consistency between primary tumours and lymph node metastases. Patients with high-tumour specific RBM3 expression treated with NAC had a significantly reduced risk of recurrence and a prolonged CSS and OS compared to NAC-untreated patients. In high-grade T24 carcinoma cells, which expressed higher RBM3 mRNA levels compared to RT4 cells, RBM3 silencing conferred a decreased sensitivity to cisplatin and gemcitabine. Transcriptomic analysis revealed potential involvement of RBM3 in facilitating cell cycle progression, in particular G1/S-phase transition, and initiation of DNA replication. Furthermore, siRBM3-transfected T24 cells displayed an accumulation of cells residing in the G1-phase as well as altered levels of recognised regulators of G1-phase progression, including Cyclin D1/CDK4 and CDK2.ConclusionsThe presented data highlight the potential value of RBM3 as a predictive biomarker of chemotherapy response in MIBC, which could, if prospectively validated, improve treatment stratification of patients with this aggressive disease.