Predictive Biomarkers Of Clinical Response Research Articles

Low CD86 expression is a predictive biomarker for clinical response to the therapeutic human papillomavirus vaccine IGMKK16E7: results of a post hoc analysis.

Although therapeutic human papillomavirus vaccines could offer a noninvasive treatment for patients with cervical intraepithelial neoplasia, none has been clinically implemented. Oral administration of the therapeutic human papillomavirus vaccine IGMKK16E7 results in the histological regression of human papillomavirus 16-positive cervical intraepithelial neoplasia 2/3 to normal (complete response). We investigated biomarkers that could predict complete response after oral administration of IGMKK16E7. Forty-two patients administered high-dose oral IGMKK16E7 in a phase I/II trial were included. Cervix-exfoliated cells were collected before vaccine administration. Gene expression of CD4, CD8, FOXP3, programmed cell death 1 protein, CTLA4, CD103, CD28, CD80, CD86, and programmed cell death 1 ligand 1 in the cells was measured by quantitative reverse transcriptase-polymerase chain reaction. Receiver operating characteristic curve analysis and Mann-Whitney tests were used to explore potential biomarkers. Pearson correlation coefficient analysis was used to correlate gene expression profiles with clinical outcome. The only predictive biomarker of vaccine response for which receiver operating characteristic curve analysis showed significant diagnostic performance with histological complete response was CD86 (area under the curve = 0.71, 95% confidence interval = 0.53 to 0.88, P = .020). Patients with complete response had significantly lower CD86 expression (CD86-low) than patients with no complete response (P = .035). The complete response rates for CD86-low and CD86-highpatients were 50% and 19%, respectively, and CD86-low patients had a significantly higher complete response rate (P = .047). Compared with all patients, the CD86-low group had a 1.5-fold increase in the complete response rate. Gene expression of CD86 and CTLA4 showed the strongest positive correlation with clinical outcomes in the incomplete response group (P < .001). Low expression of CD86 in exfoliated cervical cells can be used as a pretreatment biomarker to predict histological complete response after IGMKK16E7 administration.

DOP22 Predictive biomarkers of therapeutic response in Inflammatory Bowel Disease: a step towards personalized medicine

Abstract Background Inflammatory bowel diseases are complex conditions characterized by heterogeneity at clinical, immunological, molecular, genetic, and microbial levels. The most effective approaches to induce clinical remission and control inflammation in these diseases are biologic and JAK-inhibitor therapies. However, a significant proportion of patients do not respond to treatment. Consequently, the identification of predictive biomarkers of clinical response to the different available therapies could improve the selection of patients who may benefit from the most appropriate treatment. Methods A multiomic study (transcriptomic, proteomic, metabolomic and metagenomic) was performed in 53 patients with active Crohn´s disease (CD) and 50 patients with active ulcerative colitis (UC) before and after 14 weeks of treatment (anti-TNFα, ustekinumab, vedolizumab or tofacitinib). Responders and non-responders patients were categorized based on endoscopic findings. Samples (serum, urine, stool and intestinal biopsies) were used for RNA-seq analysis, liquid chromatography-mass spectrometry, nuclear magnetic resonance, and 16S rRNA gene sequencing. Results Our findings revealed multiple mRNAs, proteins and metabolites associated with the response to different treatments (Table 1). Differential gene expression analyses in intestinal tissue showed that the main differences were detected between responders versus non-responders UC patients to anti-TNF, vedolizumab and tofacitinib. Proteomic analysis found numerous differentially expressed proteins between the study groups. Additionally, ROC curves revealed two proteins with good predictive value to discriminate between CD and UC patients responders versus non-responders to anti-TNF treatment (AUC=0.81 and AUC=0.96, respectively). Metabolomics showed up-regulation of 21 lipoproteins in serum from CD patients responders to ustekinumab compared to non-responders. Metabolic analysis pathways identified enrichment in ketone and butyrate metabolism, mitochondrial electron transport chain, carnitine synthesis, and oxidation of chain fatty acids. Regarding metagenomic results, only differences were found in microbial composition in responders versus non-responders UC patients treated with anti-TNF (Figure 1). Conclusion This study provides early insights into shifts in gene and protein expression in intestinal tissue, alterations in serum and urine metabolites, and changes in gut microbiota as potential predictors of response to biologics and JAK-inhibitor treatment. Further research is needed to validate these results and asses their clinical significance in identifying patients most likely to benefit from each therapeutic approach.

Analysis of serum proteomics data identifies a quantitative association between beta-defensin 2 at baseline and clinical response to IL-17 blockade in psoriatic arthritis

ObjectivesDespite several effective targeted therapies, biomarkers that predict whether a patient with psoriatic arthritis (PsA) will respond to a particular treatment are currently lacking.MethodsWe analysed proteomics data from serum samples...

The negative effect of antibiotics on RCC patients with immunotherapy: A systematic review and meta-analysis.

Microbiome dysbiosis is considered a predictive biomarker of clinical response in renal cell carcinoma (RCC), which can be regulated by antibiotics (ATB). Multiple studies have shown that concomitant ATB administration has inhibitory effects on immunotherapy in RCC. This review aimed to assess the impact of ATB on patient survival and tumor response in RCC with immunotherapy. Literature evaluating the effect of ATB on immunotherapy in RCC from Cochrane Library®, PubMed®, Embase®, Scopus®, and Web of Science® were systematically searched. Hazard ratios (HR) for progression-free survival (PFS) and overall survival (OS), odds ratio (OR) for objective response rate (ORR) and primary progressive disease (PD) were pooled as effect sizes for clinical outcomes. Subgroup analysis was conducted to reveal the determinants of the effect of ATB on immunotherapy, including time windows of ATB exposure to immunotherapy initiation, ICIs treatment and study location. The leave-one-out approach was adopted to analyze the heterogeneity formulated. Cumulative meta-analysis adding by time was used to observe dynamic changes of the results. Ten studies were included in the systematic review and six studies (with n=1,104 patients) were included in the meta-analysis, four studies were excluded for overlapping patients with subsequent larger studies and lack of unique patient-level data. ATB administration was significantly correlated with shorter PFS (HR=2.10, 95%CI [1.54; 2.85], I2 = 2% after omitting study Derosa et al, 2021 detected by leave-one-out approach), shorter OS (HR=1.69, 95%CI [1.34; 2.12], I2 = 25%) and worse ORR (OR=0.58, 95%CI [0.41; 0.84]), but no difference was observed in risk of PD (OR=1.18, 95%CI [0.97; 1.44]). No significant differences existed among the subgroups for determining the determinants of ATB inhibition. Concomitant ATB with immunotherapy was associated with worse PFS, OS and ORR in RCC. No publication bias was observed in this study. https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=349577, identifier CRD42022349577.

Spatial Protein Analysis of the Tumor Microenvironment and Biomarkers in Hodgkin's Lymphoma

Introduction: The tumor microenvironment (TME) is the complex milieu of cells and molecules surrounding the tumor. Single cell methods have been used to great effect to identify subpopulations of cells that have pro- or anti-tumor properties, and selectively modulating these has great therapeutic benefits, especially in immunotherapy. However, there is limited information on the spatial organization of these subpopulations, which determines how they signal and their therapeutic potential. Single cell resolved spatial computational analysis is needed to describe the complex interactions of the TME and their effect on patient outcomes. Hodgkin's Lymphoma presents a unique spatial TME due to the sparse tumor distribution of Hodgkin's Reed-Sternberg tumor cells. Yet even though it is difficult to disentangle signaling in Hodgkin's tumor cells - which are immune derived - from the immune TME, Hodgkin's is highly receptive to checkpoint inihibitors among lymphomas and insights gleaned from the Hodgkin's TME could better inform immunotherapies across lymphomas. Materials and Methods: Here we apply Imaging Mass Cytometry (IMC), a technology to perform ~40-plex protein analysis with 1 micron resolution in tissue, to study a cohort of 260 matched samples at diagnosis and after relapse from 90 patients with relapsed/refractory Hodgkin's Lymphoma. We developed a computational pipeline to perform cell phenotyping, spatial analysis, and biostatistics, to describe tumor architecture and propose putative biomarkers of Hodgkin's clinical response and relapse. A novel feature of the pipeline is to quantify proteins and spatial analysis on the same numerical scale for each cell, to generate hybrid biomarkers. Results and Discussion: We analyzed over 7 million cells for their phenotype and spatial organization. We use IMC to describe spatial features of the tumor - cell subtypes and their positioning - that correlate to clinical outcomes. We identify proteins such as CXCR5 that correlate to survival in specific spatial contexts, and we describe spatial reorganization from diagnosis to the relapsed tumor as it relates to survival and relevant clinical factors such as MHC expression and EBV infection. A significant conceptual advance was to use spatial metrics to perform "digital biopsies", a selection of tumor regions comparable across patients. We also describe cell rosetting, a localized recruitment of immune cells to tumor that is characteristic of Hodgkin's. We validated multiple existing biomarkers in the literature using our data set and proposed novel biomarkers using IMC data. Conclusions: Spatial analysis of the HL microenvironment revealed composite features of the TME that predict clinical outcomes. These features cannot be described using single cell tools or low-plexed imaging, and represent a truer picture of HL biology. The pipeline developed here can be universally applied to other spatial protein data for biomarker discovery and analysis, and the biomarkers proposed here will be validated with IHC. Figure caption: IMC analysis of Hodgkin's Lymphoma. A. IMC generates a 40-plex protein image. B, C. The image is converted to single cells and analyzed for protein and spatial features. D. A UMAP of cell phenotypes and the spatial interactions of specific phenotypes. E. The spatial and protein values are used to generate predictive biomarkers of clinical response. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

CD25, Soluble CD25, and CCL17 As Potential Predictors of Clinical Response to Camidanlumab Tesirine in Patients with Relapsed/Refractory Classical Hodgkin Lymphoma

Introduction: Camidanlumab tesirine (Cami), an antibody-drug conjugate targeting CD25, has shown single-agent anti-tumor activity and manageable toxicity in a phase 2 trial in relapsed/refractory (R/R) classical Hodgkin lymphoma (cHL; Carlo-Stella, et al. 2022). In the phase 1 cHL trial, biopsy tumor cell CD25 histoscores (H-scores) were significantly higher in responders vs nonresponders and low baseline soluble CD25 (sCD25) was possibly related to response (Hamadani, et al. 2021). Separately, circulating CCL17 has been correlated with response to therapies for cHL (Plattel, et al. 2016). Objective: To assess CD25, sCD25, and CCL17 as predictive biomarkers of clinical response to Cami in patients (pts) with R/R cHL. Methods: This analysis was based on the open-label, multicenter, phase 2 study in pts with R/R cHL after ≥3 prior lines of therapy (or ≥2 in pts ineligible for SCT; NCT04052997). CD25 expression was assessed by immunohistochemistry (IHC) and H-score in all archival tumor biopsies and a subset of recent biopsies (archival biopsies taken after the last systemic anticancer treatment, including SCT if applicable); serum sCD25 and CCL17 were assessed by qualified immunoassays prior to Cami infusion on day 1 of each cycle. Cami may interfere with the sCD25 assay, underestimating post-baseline (BL) sCD25. CD25 IHC, BL sCD25 and CCL17, and changes in sCD25 and CCL17 from BL were compared between responders and nonresponders. Receiver operating characteristic (ROC) analyses were performed to evaluate the predictive potential of these measures as response biomarkers. Results: Assessments of CD25, sCD25, and CCL17 in pts with a best overall response (BOR) of complete or partial response (responders) and stable disease or PD (nonresponders) were performed for 111 of 117 pts treated with Cami (6 pts were not evaluable for BOR). No significant difference in tumor cell CD25 H-score was observed between responders and nonresponders in all (n=99) or recent biopsies (n=23; p>0.05 for both), despite higher median levels in responders. BL sCD25 (n=108) was significantly higher in nonresponders than responders (p=0.018; Fig 1); though, overlapping values were observed in a substantial number of pts. There was also a significantly greater decrease in sCD25 in nonresponders vs responders from BL to C3D1 (n=79; median 0.53 vs 1.02; p=0.018). While there was no significant difference in BL CCL17 levels (n=106), there was a significantly greater decrease in BL CCL17 in responders vs nonresponders at C2D1 (n=97; median 0.50 vs 0.81; p<0.01 log10 change). In ROC analyses in a subset of 96 pts with CD25 IHC and BL sCD25 results available, sCD25 alone showed a modest predictive potential (area under curve [AUC]=0.65) that was not improved with the addition of tumor cell CD25 H-score (AUC=0.67). In a subset of 76 pts with results available for sCD25 and CCL17 at BL and C2D1, ROC curves of BL sCD25, BL sCD25 plus BL CCL17, sCD25 change from BL, and CCL17 change from BL showed modest predictive potential (AUC<0.7 for all). A better AUC was found in a model including change from BL at C2D1 of sCD25 and CCL17 (AUC=0.74). This AUC was further increased in a model of BL sCD25 and CCL17 plus their change from BL at C2D1 and a model of BL sCD25 plus sCD25 and CCL17 change from BL at C2D1 (AUC=0.76 for both; Fig 2). The latter model had a higher Youden index. Conclusions: Higher median tumor cell CD25 H-score was seen in responders vs nonresponders in all and recent biopsies but the differences did not reach significance. These data warrant further investigations as phase 1 data showed significantly higher tumor CD25 expression in responders (Hamadani, et al. 2021). Nonresponders had significantly higher BL sCD25; however, this parameter alone has insufficient predictive potential for Cami efficacy. Predictive models including BL sCD25 plus sCD25 and CCL17 change from BL at C2D1 (± BL CCL17) showed the best ROC AUCs but need further refining to be clinically relevant. However, given the high overall and early responses in most pts treated with Cami, biomarkers predicting response at C2D1 are suboptimal unless reaching a higher sensitivity and specificity. Investigation of sCD25 in the context of the Cami mode of action and exposure-response and the potential value of sCD25 and CCL17 changes as early indicators of Cami's effect and as biomarkers for disease monitoring and response are ongoing. Funding: ADC Therapeutics SA; writing: CiTRUS Health Group. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Novel pathophysiological insights in autoimmune myasthenia gravis.

This review summarizes recent insights into the immunopathogenesis of autoimmune myasthenia gravis (MG). Mechanistic understanding is presented according to MG disease subtypes and by leveraging the knowledge gained through the use of immunomodulating biological therapeutics. The past two years of research on MG have led to a more accurate definition of the mechanisms through which muscle-specific tyrosine kinase (MuSK) autoantibodies induce pathology. Novel insights have also emerged from the collection of stronger evidence on the pathogenic capacity of low-density lipoprotein receptor-related protein 4 autoantibodies. Clinical observations have revealed a new MG phenotype triggered by cancer immunotherapy, but the underlying immunobiology remains undetermined. From a therapeutic perspective, MG patients can now benefit from a wider spectrum of treatment options. Such therapies have uncovered profound differences in clinical responses between and within the acetylcholine receptor and MuSK MG subtypes. Diverse mechanisms of immunopathology between the two subtypes, as well as qualitative nuances in the autoantibody repertoire of each patient, likely underpin the variability in therapeutic outcomes. Although predictive biomarkers of clinical response are lacking, these observations have ignited the development of assays that might assist clinicians in the choice of specific therapeutic strategies. Recent advances in the understanding of autoantibody functionalities are bringing neuroimmunologists closer to a more detailed appreciation of the mechanisms that govern MG pathology. Future investigations on the immunological heterogeneity among MG patients will be key to developing effective, individually tailored therapies.

Abstract LB107: Novel transcriptional signatures associated with antitumor activity in vidutolimod (vidu)-treated patients (pts) with anti-PD-1-refractory melanoma and non-small cell lung cancer (NSCLC)

Abstract Background: Vidu is a first-in-class CpG-A TLR9 agonist in a virus-like particle that activates plasmacytoid dendritic cells (pDC), thus bridging innate and adaptive immunity. Intratumoral (IT) vidu alone or in combination with intravenous (IV) anti-PD-(L)1 has shown evidence of antitumor activity in pts with anti-PD-(L)1-refractory melanoma or NSCLC. As biomarkers associated with anti-PD-(L)1 response were not predictive of vidu activity, we sought to identify novel transcriptional signatures. Methods: RNA-Seq was performed on baseline biopsies from pts with anti-PD-(L)1-refractory melanoma (NCT02680184; RECIST v1.1 responders [R; n=20]; nonresponders [NR; n=78; 30 pts with stable disease + 48 pts with progressive disease [PD]]) or NSCLC (NCT03438318; N=11) treated with vidu ± anti-PD-(L)1. IFNg18 signature was used to characterize melanomas as non-T cell-inflamed (non-Tinfl), T cell-inflamed (Tinfl), or intermediate. Gene Set Enrichment Analysis (GSEA) using MSigDB and other signatures (&amp;gt;19,000) was performed on non-Tinfl melanoma in R vs NR (false discovery rate [FDR] &amp;lt;0.25). Deconvolution of immune cells was performed using TIMER2.0. Prediction models were generated using QLattice. Signatures were characterized using both publicly available bulk and single-cell (sc) RNA-Seq datasets of pDC subsets or PD-1/CTLA-4 blockade response datasets. Results: GSEA on non-Tinfl melanoma biopsies revealed 2 gene signatures (COPII vesicle and Golgi targeting) most strongly associated with R (FDR &amp;lt;0.16). Leading edge analysis of these signatures identified 35 common core genes (CC) that strongly differentiated RECIST 1.1 R vs PD. As independent validation, CC enrichment was also significantly associated with R in intermediate melanoma (p=0.009) and with tumor shrinkage in NSCLC (p=0.027). CC was significantly associated with R to vidu single-agent or combination treatment, but not with clinical baseline prognostic factors or IFNg18. In Tinfl melanoma, CC was not associated with R, but myeloid signatures were significantly associated with NR. A model based on CC and transcription factor ELF2 predicted R in melanoma (AUC 0.93 [95% CI 0.82-1.00]). In public datasets, CC was not associated with R to PD-1/CTLA-4 blockade, but CC was highly expressed in the type I interferon-secreting subset of pDCs and some myeloid cells. CC expression was prevalent in most tumor types and was independent of IFNg18 in TCGA. Conclusion: In pts with anti-PD-1-refractory melanoma or NSCLC, transcriptional signatures of COPII vesicle and Golgi targeting (functionally related to TLR9 activation) were associated with antitumor activity of IT vidu ± IV anti-PD-(L)1. Ongoing bulk and sc RNA-Seq analyses may clarify the underlying biology and the signature’s potential role as a predictive biomarker for clinical response to vidu. Citation Format: Hong Liu, Luping Zhao, Ping Zheng, Riyue Bao, Jason J. Luke, Marcelo V. Negrao, Shakoora A. Sabree, George J. Weiner, Sujatha Kumar, Dmitri Bobilev, James E. Wooldridge, Arthur M. Krieg. Novel transcriptional signatures associated with antitumor activity in vidutolimod (vidu)-treated patients (pts) with anti-PD-1-refractory melanoma and non-small cell lung cancer (NSCLC) [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 LB107.

DNA Damage Response Inhibitor and Anti-PD-L1 Therapy for Prostate Cancer: Development of Predictive Biomarkers

Despite significant benefit for other cancer subtypes, immune checkpoint therapy (ICT) has not yet been shown to significantly improve outcomes for men with castrationresistant prostate cancer (CRPC). Previous studies have shown that DNA damage response (DDR) deficiency, via genetic alteration and/or pharmacologic induction using DDR inhibitors (DDRi), may improve ICT response in prostate cancer and potentially oither solid tumors, in part due to the induction of innate immune activation and regulation of the tumor cell– and/or immune cell–expressed B7 immune checkpoint protein PD-L1, which can be targeted by anti-PD-L1 agents. With this information and orientation, development of PD-L1 expression as a potential predictive biomarker for clinical response to ICT therapy has received widespread attention. However, the complex heterogeneous expression patterns of tumor cell–expressed PD-L1 and its related posttranslational modification in tumors substantially complicate detection and interpretation of PD-L1 in clinical samples using IHC and currently available PD-L1 antibodies. We discuss the potential roles of PD-L1 in prostate cancer and potential approaches to more accurately detect its expression in clinical tumor samples. Additional understanding of PD-L1 functions and development of improved methods to detect PD-L1 as a predictive biomarker for prostate cancer immunotherapy will likely benefit patients with advanced prostate cancer.

045 NF-κB phosphorylation in type-2 dendritic cells at baseline is a blood predictive biomarker of clinical response to adalimumab in psoriasis

Biological therapies have transformed the management of psoriasis, but clinical response is variable and there is an unmet clinical need to identify predictive biomarkers. Whilst newer biologics have been licensed, the anti-TNF drug adalimumab remains a first-line intervention in psoriasis, given its effectiveness, well-established safety profile and, with the advent of biosimilars, significantly reduced costs. Nevertheless, the cellular and molecular mechanisms underpinning its clinical efficacy are ill understood and no companion diagnostic exists to guide prescription.

The Gut Microbiome and Cancer Immunotherapy: Can We Use the Gut Microbiome as a Predictive Biomarker for Clinical Response in Cancer Immunotherapy?

Simple SummaryThe current review assessed the effects of the gut microbiome on clinical outcomes of immunotherapy and related adverse events (AEs) in cancer patients. Studies (n = 10) consistently reported that the gut microbiome prior to administering immune checkpoint inhibitors (ICIs) was associated with enhanced efficacy of ICIs and reduced AEs. Recent fecal microbiome transplant (FMT) studies demonstrated the modulatory effects of FMT on the composition and diversity of the gut microbiome in patients with refractory cancers and the potential to improve the efficacy of ICIs.Background: Emerging evidence suggests that gut microbiota influences the clinical response to immunotherapy. This review of clinical studies examines the relationship between gut microbiota and immunotherapy outcomes. Method: A literature search was conducted in electronic databases Medline, PubMed and ScienceDirect, with searches for “cancer” and “immunotherapy/immune checkpoint inhibitor” and “microbiome/microbiota” and/or “fecal microbiome transplant FMT”. The relevant literature was selected for this article. Results: Ten studies examined patients diagnosed with advanced metastatic melanoma (n = 6), hepatocellular carcinoma (HCC) (n = 2), non-small cell lung carcinoma (NSCLC) (n = 1) and one study examined combination both NSCLC and renal cell carcinoma (RCC) (n = 1). These studies consistently reported that the gut microbiome profile prior to administering immune checkpoint inhibitors (ICIs) was related to clinical response as measured by progression-free survival (PFS) and overall survival (OS). Two studies reported that a low abundance of Bacteroidetes was associated with colitis. Two studies showed that patients with anti-PD-1 refractory metastatic melanoma experienced improved response rates and no added toxicity when receiving fecal microbiota transplant (FMT) from patients with anti-PD-1 responsive disease. Conclusions: Overall, significant differences in the diversity and composition of the gut microbiome were identified in ICIs responders and non-responders. Our findings provide new insights into the value of assessing the gut microbiome in immunotherapy. Further robust randomized controlled trials (RCTs) examining the modulatory effects of the gut microbiome and FMT on ICIs in patients not responding to immunotherapy are warranted.

Tumor Mutational Burden, Toxicity, and Response of Immune Checkpoint Inhibitors Targeting PD(L)1, CTLA-4, and Combination: A Meta-regression Analysis.

Tumor mutational burden (TMB) has emerged as a potential predictive biomarker for clinical response to ICI therapy, but whether TMB also predicts toxicity remains unknown. We investigated the relationship between TMB, objective response rate (ORR), overall survival (OS), and toxicity for ICI therapy across multiple cancer types. We searched MEDLINE, PubMed, and ASCO/ESMO/AACR meetings for clinical trials of anti-PD(L)1, CTLA-4, or combination in 29 cancer types. We assessed ICI administered, responses (complete or partial response), median OS, OS HR, and grade 3/4 toxicity. We conducted a systematic review, meta-analysis and meta-regression using tumor level TMB data from Foundation Medicine. One hundred seventeen clinical trials, which included 12,450 patients treated with ICI therapy were analyzed. Meta-regression analysis revealed that TMB was significantly associated with ORR for anti-PD(L)1, anti-CTLA-4, and combination (P < 0.0001 for all), but not associated with toxicity in all treatment groups. OS data were unavailable for most studies included in our meta-analysis, and the relationship between TMB and OS in this subset was not significant (P = 0.26). In high TMB tumor types (≥10 mut/megabase) the improvement of ORR and increase in grade 3/4 toxicity with combination ICI therapy as compared with PD(L)1 monotherapy were 21.13% and 25.41%, respectively, as compared with 3.73% and 18.78% in low TMB tumor types (<10 mut/megabase). There is a positive association between TMB and clinical response with anti-PD(L)1, anti-CTLA-4, and combination ICIs, but no association between TMB and toxicity. These results imply a favorable risk/benefit ratio for ICIs in tumors with a higher TMB.

Abstract LB-011: Patient-derived organoids may facilitate precision medicine in pancreatic cancer: Demonstrating feasibility in the context of a multi-center clinical trial

Abstract Background: Systemic disease control is the principle driver of prognosis in patients with pancreatic cancer (PDAC). Currently, there are no predictive biomarkers of clinical response to guide selection of an optimal treatment approach. Our work explores the logistics and feasibility of generating patient tumor-derived models to serve as biomarkers. Methods: Patients with borderline resectable or locally advanced PDAC enrolled to participate in a multi-institutional prospective, randomized, trial were eligible. Pretreatment core needle biopsies from diagnostic endoscopy were shipped via commercial post to a centralized organoid laboratory. Patient-specific organoids (PDO) were established as previously described by our group. Putative predictive biomarkers of clinical treatment response, including whole-exome sequencing (WES), RNA-sequencing, and pharmacotyping (chemotherapeutic sensitivity testing) were performed prospectively. Putative predictive biomarkers will be compared to clinical outcomes when approved by the trial's Data Safety Monitoring Board. Results: Approximately 40% (42/98) of patients participated in tissue acquisition for organoid development. Culture maturation can be described in three relevant phases: establishment, expansion, and characterization. The establishment phase describes the emergence of viable ductal organoid development in the setting of a culture being cleared of other components of the tumor microenvironment. During these first 3-5 passages, there are numerous potential pitfalls, including technical and logistical challenges. Out of 42, 26 (62%) PDOs were successfully established. Notably, the establishment phase appears to be an area of the greatest improvement, as the rates of successful establishment improve while the collaborative team builds experience. Expansion involves biomass accumulation in clean culture. Of those that succeeded in establishment, 77% (20/26) entered in expansion phase. We can currently report 15 PDOs having reached the characterization phase (WES, RNA-seq with subtyping, and/or pharmacotyping). Based on RNA expression profile, 10/14 were classified as classical and 4 as basal-like subtype. Mean time to pharmacotyping was 179 days. The PDOs showed a variable population distribution of sensitivity to standard-of-care chemotherapeutics (gemcitabine, paclitaxel, irinotecan, 5-FU, oxaliplatin). Conclusions: Developing a living organoid biobank from PDAC biopsies in a multi-center trial setting is a tractable approach in precision medicine initiatives. Amongst a collaborative team of physician scientists, the establishment phase is critical. PDOs from pre-treatment biopsies can be individually characterized within a clinically relevant time frame and unique tumor-specific ex vivo-derived therapeutic sensitivities can be assessed. Citation Format: Toni T. Seppälä, Jacquelyn W. Zimmerman, Noah Rozich, Alex Blair, Ammar Javed, John L. Cameron, William R. Burns, Jin He, David Tuveson, Christopher L. Wolfgang, David P. Ryan, Alec Kimmelman, Joseph M. Herman, Wells Messersmith, Theodore S. Hong, David T. Ting, Richard Andrew Burkhart. Patient-derived organoids may facilitate precision medicine in pancreatic cancer: Demonstrating feasibility in the context of a multi-center clinical trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-011.

Peripheral blood cell count ratios are predictive biomarkers of clinical response and prognosis for non-surgical esophageal squamous cell carcinoma patients treated with radiotherapy.

BackgroundPeripheral blood cell count ratios, including the neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and lymphocyte‐to‐monocyte ratio (LMR), have been reported to be prognostic factors in many malignancies as markers of inflammation and immune status. The aim of this study was to determine whether NLR, PLR, or LMR can be clinical response and prognostic biomarkers of non‐surgical esophageal squamous cell carcinoma (ESCC) patients treated with radiotherapy.Methods193 non‐surgical ESCC patients who underwent radiotherapy were retrospectively analyzed. The peripheral blood cell count ratios were obtained before, during (weekly) and at the end of the treatment. Then, we compared the subsequent results with the corresponding pretreatment values and computed the rates of change, which were defined as cNLR, cPLR, and cLMR. Univariate and multivariate Cox regression analyses were used for overall survival (OS). Ordinal logistic regression was used to analyze the clinical response.ResultsIn multivariate analysis, cNLR at week 4(P = .026) and week 5(P = .025) during radiotherapy were significantly associated with OS, along with BMI, tumor stage, tumor length, tumor location, and grade of adverse events. Besides, BMI, tumor stage, tumor length, adverse event grade, cNLR at week 4(P = .044) and week 5(P = .013), and cPLR at week 4(P = .034) and week 5(P = .015) were significantly associated with the clinical response in the multivariate logistic regression analysis.ConclusionsThe cNLR at weeks 4 and 5 was negatively correlated with the OS and clinical response of non‐surgical ESCC patients treated with radiotherapy. The elevated cPLR at weeks 4 and 5 was only related to poor clinical response.

Quantitative and Spatial Analysis of CD8+/PD-1 Tumor-Infiltrating Lymphocytes as a Predictive Biomarker for Clinical Response of Melanoma In-Transit Metastases to Topical Immunotherapy

BackgroundMelanoma in-transit metastases (ITMs) are a challenge to treat and associated with systemic disease and poor prognosis. Topical diphencyprone (DPCP), a potent contact sensitizer, is an established treatment for melanoma ITMs. This exploratory study investigated the utility of BRAF mutation status, CD8, PD-1, PD-L1, and TILs distribution as biomarkers for response of ITMs to topical immunotherapy (DPCP).MethodsThe ITM deposits of 40 patients treated with DPCP were subjected to biomarker analysis for BRAF status, CD8 and PD-1 expression on tumor-infiltrating lymphocytes (TILs), and tumor PD-L1 expression. Response to DPCP and overall survival (OS) were compared by biomarker status.ResultsAfter 12 weeks, 10 patients (25%) had a complete response, 12 patients (30%) had a partial response, and 18 patients (45%) had no response. No significant association was found between any individual biomarker and response to DPCP or OS. The BRAF mutation rate was 25% (10/40). All the patients with a complete response had BRAF wild-type tumor. Peritumoral CD8+ T-cells were associated with complete response (P = 0.041). Both CD8+ and PD-1 expressions were highly correlated (P < 0.0001), and the highest levels of PD-1 expression were detected at the peritumoral interface (P = 0.0004). Only two cases were PD-L1-positive, and both had a complete response to DPCP (P = 0.043).ConclusionPatients who have BRAF wild-type tumor are more likely to experience a complete response to DPCP. Peritumoral TILs and PD-1 expressions may predict a better response to DPCP. Expression of PD-L1 may be associated with a complete response to DPCP. A larger prospective study is required.

Taxane resistance in prostate cancer is mediated by decreased drug-target engagement.

Despite widespread use of taxanes, mechanisms of action and resistance in vivo remain to be established, and there is no way of predicting who will respond to therapy. This study examined prostate cancer (PCa) xenografts and patient samples to identify in vivo mechanisms of taxane action and resistance. Docetaxel drug-target engagement was assessed by confocal anti-tubulin immunofluorescence to quantify microtubule bundling in interphase cells and aberrant mitoses. Tumor biopsies from metastatic PCa patients obtained 2 to 5 days after their first dose of docetaxel or cabazitaxel were processed to assess microtubule bundling, which correlated with clinical response. Microtubule bundling was evident in PCa xenografts 2 to 3 days after docetaxel treatment but was decreased or lost with acquired resistance. Biopsies after treatment with leuprolide plus docetaxel showed extensive microtubule bundling as did biopsies obtained 2 to 3 days after initiation of docetaxel or cabazitaxel in 2 patients with castration-resistant PCa with clinical responses. In contrast, microtubule bundling in biopsies 2 to 3 days after the first dose of docetaxel was markedly lower in 4 nonresponding patients. These findings indicate that taxanes target both mitotic and interphase cells in vivo and that resistance is through mechanisms that impair drug-target engagement. Moreover, the findings suggest that microtubule bundling after initial taxane treatment may be a predictive biomarker for clinical response.

Abstract A5: Genome-wide methylation pattern predicts clinical benefit of immune checkpoint blockade therapy in NSCLC patients

Abstract Immune checkpoint blockade (ICB) therapy for non-small cell lung cancer (NSCLC) has been gaining wide attention as the next-generation cancer therapy that can bolster patients’ immune system to better fight cancer. However, only a small subset of patients reap clinical benefit. Thus, it is crucial to unravel molecular determinants of response to ICB therapy. Although several predictive biomarkers of clinical response to ICB therapy have been proposed, such as mutation burden and expression of key genes (e.g., PD-L1), these biomarkers are not sufficient to accurately predict response to ICB therapy. Here, we aim to facilitate prediction of therapeutic response by analyzing DNA methylation pattern difference between responders and nonresponders on a genome-wide scale. To this end, we performed DNA methylation array (Illumina 850K/EPIC platform), exome-, and RNA-seq experiments for 60 NSCLC patients who have received ICB therapy from Samsung Medical Center. We found 434 differentially methylated genes between nonresponders and responders where 73 and 361 were hyper- and hypo- methylated, respectively, in nonresponders. Notably, immune pathways enriched with hyper-methylated genes significantly overlapped with those enriched with underexpressed genes in nonresponders, suggesting that promoter methylation-mediated immune pathway silencing affects immune response and clinical benefit. Furthermore, we identified 474 protein interaction networks where two or three hypermethylated genes showed significantly mutually exclusive pattern across nonresponders. Importantly, 34 protein networks were associated with progression-free survival. Finally, based on the methylation data from our cohort, we have built LASSO-Cox regression model to predict prognosis of patients. The model, which comprises 8 genes, was validated in two independent cohorts (81 NSCLC patients receiving ICB therapy from Bellvitge Biomedical Research Institute and 479 NSCLC patients from TCGA), demonstrating the importance of the methylation pattern of 8 genes in determining clinical benefit. In conclusion, methylation pattern can provide insight into clinical benefit of ICB therapy. In this study, we have extensively characterized methylation difference between responders and nonresponders on a genome-wide scale. We also demonstrate that gene hypermethylation significantly correlates with immune-related pathways’ silencing. Furthermore, our machine learning model built upon methylation data provides predictive power of clinical benefit. Thus, in addition to known biomarkers (e.g., mutation burden), methylation pattern should be considered when predicting benefit of ICB therapy. Citation Format: Jeongyeon Kim, Hyeon Gu Kang, Jae Soon Park. Genome-wide methylation pattern predicts clinical benefit of immune checkpoint blockade therapy in NSCLC patients [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A5.

Circulating Levels of PD-L1 in Mesothelioma Patients from the NIBIT-MESO-1 Study: Correlation with Survival.

Targeting of the programmed cell death protein (PD)-1/programmed death-ligand 1 (PD-L1) axis has shown a significant clinical impact in several tumor types. Accordingly, our phase II NIBIT-MESO-1 study demonstrated an improved clinical efficacy in mesothelioma patients treated with the anti-PD-L1 durvalumab combined with the anti-cytotoxic T-lymphocyte antigen (CTLA)-4 tremelimumab, as compared to tremelimumab alone. Due to the promising therapeutic activity of immune check-point inhibitors (ICIs) in mesothelioma patients, the identification of biomarkers predictive of response to treatment is of crucial relevance. The prognostic role of soluble PD-L1 (sPD-L1) proposed in cancer patients prompted us to investigate this protein in sera from mesothelioma patients (n = 40) enrolled in the NIBIT-MESO-1 study. A significant (p < 0.001) increase in sPD-L1 levels was detected in patients after the first cycle and during therapy vs. baseline. A longer overall survival (OS) was observed in patients with sPD-L1 concentrations below (at baseline, d1C2, d1C5 (p < 0.01)) or FC values above (p < 0.05 at d1C2, d1C3, d1C5) their statistically calculated optimal cut-offs. On the basis of these initial results, the specific role of CTLA-4-, PD-L1-, or PD-1-targeting on sPD-L1 release was then investigated in sera from 81 additional ICI-treated solid cancer patients. Results showed a significant (p < 0.001) increase of sPD-L1 levels during therapy compared to baseline only in anti-PD-L1-treated patients, supporting the specific involvement of PD-L1 targeting in the release of its soluble form. Our findings suggest that sPD-L1 represents a predictive biomarker of clinical response to anti-PD-L1 cancer immunotherapy.

Early Changes in Circulating FGF19 and Ang-2 Levels as Possible Predictive Biomarkers of Clinical Response to Lenvatinib Therapy in Hepatocellular Carcinoma.

Predictive biomarkers of the response of hepatocellular carcinoma (HCC) to Lenvatinib therapy have not yet been clarified. The aim of this study was to identify clinically significant biomarkers of response to Lenvatinib therapy, to target strategies against HCC. Levels of circulating angiogenic factors (CAFs) were analyzed in blood samples collected at baseline and after introducing lenvatinib, from 74 Child-Pugh class A HCC patients who received lenvatinib. As CAF biomarkers, serum vascular endothelial growth factor (VEGF), fibroblast growth factor 19 (FGF19), FGF23, and angiopoietin-2 (Ang-2) were measured using enzyme-linked immunosorbent assays. Results: Significantly increased FGF19 (FGF19-i) levels and decreased Ang-2 (Ang-2-d) levels were seen in Lenvatinib responders as compared to non-responders (ratio of FGF19 level at 4 weeks/baseline in responders vs. non-responders: 2.09 vs. 1.32, respectively, p = 0.0004; ratio of Ang-2 level at four weeks/baseline: 0.584 vs. 0.810, respectively, p = 0.0002). Changes in FGF23 and VEGF levels at four weeks versus baseline, however, were not significantly different in responders versus non-responders. In multivariate analysis, the combination of serum FGF19-i and Ang-2-d was the most independent predictive factor for Lenvatinib response (Odds ratio, 9.143; p = 0.0012). Furthermore, this combination biomarker showed the greatest independent association with progression-free survival (Hazard ratio, 0.171; p = 0.0240). Early changes in circulating FGF19 and Ang-2 levels might be useful for predicting clinical response and progression-free survival in HCC patients on Lenvatinib therapy.

Prediction of neuroblastoma cell response to treatment with natural or synthetic retinoids using selected protein biomarkers.

Although the administration of retinoids represents an important part of treatment for children suffering from high-risk neuroblastomas, approximately 50% of these patients do not respond to this therapy or develop resistance to retinoids during treatment. Our study focused on the comparative analysis of the expression of five genes and corresponding proteins (DDX39A, HMGA1, HMGA2, HOXC9 and PBX1) that have recently been discussed as possible predictive biomarkers of clinical response to retinoid differentiation therapy. Expression of these five candidate biomarkers was evaluated at both the mRNA and protein level in the same subset of 8 neuroblastoma cell lines after treatment with natural or synthetic retinoids. We found that the cell lines that were HMGA2-positive and/or HOXC9-negative have a reduced sensitivity to retinoids. Furthermore, the experiments revealed that the retinoid-sensitive cell lines showed a uniform pattern of change after treatment with both natural and sensitive retinoids: increased DDX39A and decreased PBX1 protein levels. Our results showed that in NBL cells, these putative protein biomarkers are associated with sensitivity or resistance to retinoids, and their endogenous or induced expression can distinguish between these two phenotypes.