Identification of a Novel Nomogram to Predict Progression Based on the Circadian Clock and Insights Into the Tumor Immune Microenvironment in Prostate Cancer

The success of immune checkpoint inhibitors rests on biomarkers such as tumor mutation burden (TMB) and microsatellite instability (MSI), both FDA-approved predictors of anti PD-1/L1 therapy benefit. Tissue biopsies often collected once in the metastatic setting through an invasive procedure, or archived primary tumor tissue often collected much prior to treatment consideration, are the specimen types of choice for biomarker identification. The tissue sample originates from a limited region of one disease site, which may limit its usefulness given intra-patient tumor heterogeneity. TMB and MSI measurement by liquid biopsy, including proteins, circulating tumor cells (CTC), and cell-free circulating tumor DNA (ctDNA), is an attractive, minimally-invasive way to obtain a real-time picture of the entire disease. While TMB and MSI assessment from ctDNA have been reported, their measurement can be limited by low ctDNA tumor fraction. Single-cell next generation sequencing of CTC, on the other hand, is a particularly well-suited, but largely unexplored method of measuring TMB and MSI to complement tissue and ctDNA for better overall specificity of detection. In this proof-of-concept study, we show the ability to detect single-cell TMB and MSI. We analyzed 14 CTC and 4 ctDNA samples from 6 metastatic breast cancer patients, as well as 3 single cells and 1 cell pellet sample each from HCT-116 (MSI-High) and WiDr (MSI-Low) cell lines. CTC and cell line cells were enriched with the CellSearch® system and/or isolated with the DEPArray™ system. Whole genome amplified single-cell DNA was sequenced with the Oncomine Comprehensive Assay covering ~500 genes and 1.1Mb of genomic space. TMB and MSI scores obtained in CTC and ctDNA were compared to those measured in matched clinical tissue biopsies. Single-cell TMB scores and MSI status were assessable in all CTC tested. CTC TMB scores were highly concordant with the matched tissue samples (r=1.00), as were ctDNA TMB scores (r=0.98) in patients with assessable TMB scores in both biospecimen types compared. Importantly, TMB was detectable in CTC from one patient whose tissue sample was inadequate for clinical sequencing, and from another patient with inadequate, low tumor fraction ctDNA. Intriguingly, one patient harbored 3 TMB-high and 2 TMB-low CTC, potentially indicating intra-patient TMB heterogeneity. The known MSI-low status from clinical tumor tissue sequencing was correctly detected in CTC and ctDNA from all patients. MSI status and scores from single cells of HCT-116 and WiDr cell lines purified with the DEPArray™ system (mimicking CTC isolation), perfectly matched that of the corresponding cell pellet samples (r=1.00). Taken together, these data suggest the potential validity and continued interrogation of potential utility of CTC TMB and MSI detection to complement tissue and ctDNA in guiding checkpoint inhibitor immunotherapy. Citation Format: Andi K. Cani, Emily M. Dolce, Chia-Jen Liu, Brittany Rupp, Elizabeth P. Darga, Costanza Paoletti, Dafydd G. Thomas, Yi-Mi Wu, Dan R. Robinson, Sunitha Nagrath, Arul M. Chinnaiyan, Scott A. Tomlins, Aaron M. Udager, John M. Carethers, Erin F. Cobain, Daniel F. Hayes. Assessment of tumor mutation burden and microsatellite instability by single-cell circulating tumor cell genomic profiling [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 611.

Immunotherapy can improve survival in a variety of cancers by modulating the interaction between tumors and the tumor immune microenvironment (TIME). V-set and transmembrane domain containing 2 like (VSTM2L) regulates interleukin (IL)-4 signaling pathway—which involves immune-related factors—and has been linked to some cancers. However, the expression profile and prognostic significance of VSTM2L in different cancers as well as its relationship to the TIME are not known. This study investigated the pan-cancer expression profile, prognostic value, and immunologic relevance of VSTM2L. VSTM2L expression in different cancers was analyzed using the Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), Tumor Immune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA), and Genotype–Tissue Expression (GTEx) portal. We examined the association between VSTM2L expression and clinical outcomes by Kaplan–Meier and Cox regression analyses using TCGA and Kaplan–Meier Plotter, and the results were validated in a Gene Expression Omnibus cohort. The correlations between VSTM2L expression and immune cell infiltration, immunomodulators, tumor mutation burden (TMB), microsatellite instability (MSI), and immune and stromal scores across cancers were analyzed using TCGA, TIMER, and Tumor–Immune System Interactions and Drugbank databases (TISIDB). The results showed that VSTM2L expression varied across cancers and its aberrant expression was associated with clinical outcomes: upregulation of VSTM2L was positively associated with advanced stage and reduced overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI), and disease-free interval (DFI) in stomach adenocarcinoma (STAD); and its upregulation was associated with early-stage disease and improved OS, DSS, PFI, and DFI in kidney renal papillary cell carcinoma (KIRP). VSTM2L expression level was correlated with immune cell infiltration, expression of immunomodulators, TMB, MSI, and immune and stromal scores in multiple cancers. In conclusion, VSTM2L has prognostic value in various cancers and can predict both poor (STAD) and good (KIRP) outcomes. The relationship between VSTM2L expression and immune markers suggests a role in modulating the TIME.

Introduction: NGS-based tests that include biomarkers such as microsatellite instability (MSI) and tumor mutational burden (TMB) increase patient access to immune checkpoint inhibitors and improve the personalization of cancer care. Tumor-only NGS tests of biomarkers typically rely on germline reference sets in order to subtract germline variants from biomarker scores. Many germline reference sets lack sufficient population diversity to ensure consistent test quality for all patients. Informatic strategies such as somatic/germline zygosity prediction are often necessary to reduce bias introduced from germline variation and population-specific artefacts and mitigate the risk of inflating TMB and MSI scores. Methods: We sequenced germline samples from a curated set of research-consented patients representing diverse ancestries on a hybrid capture panel (N=1047 patients, from 10 populations). The tested groups included: African-American=416, Hispanic=162, Ashkenazi=130, Asian=127, Caucasian=85, Middle Eastern=44, Native American=27, Pacific Islander=25, Indian=25, and African=6. We confirmed that both rare population-specific alleles and population-specific technical artefacts contributed to spurious results from biomarker score inflation in underrepresented populations. To mitigate bias and universally improve scores in both MSI and TMB, we combined 1) an ancestry-diverse panel of germline samples assembled from the targeted panel, 2) population-specific public database filtering, 3) informatic germline variant subtraction, and 4) a heuristic for population-neutral target space selection. Results: Inter-population comparisons of MSI scores (Kruskal-Wallis p of 4.6*10^-69) and TMB scores (Kruskal-Wallis p of 3.7*10^-6) differed significantly in mutation signature errors prior to the mitigation work described in the methods. Application of the germline knowledge base filters and population-neutral target selection reduced the overall error rate (51% for TMB and 16% for MSI), and reduced inter-population score differences. TMB error rates reduced most for African, Pacific Islander, and African-American patients (100%, 71%, and 58%, respectively). Conversely, MSI error rates reduced most for Caucasian, Hispanic, and Asian patients (24%, 21%, and 20%, respectively). Conclusion: The incorporation of a diverse population set in this study empowered development of a more equitable bioinformatic approach, and significantly improved TMB and MSI determination across all populations. Qualitative differences in the sources of error per population and per biomarker type were associated with differing responses to each mitigation strategy. ​​These findings underscore the technical and clinical value of incorporating diverse ancestry reference sets and identifying non-biased assay targets to optimize personalized therapy selection. Citation Format: Elizabeth R. Starks, Victoria Carlton, Veena Rajaraman, Katya Kosheleva, Tamsen Dunn, Nick Kamps-Hughes, Laurie Gay, Meaghan Russell, Sarah Albritton, John J. Vincent, Nhu Ngo. Increasing equity in MSI and TMB biomarker testing with diverse datasets and personalized informatics [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 1230.

e14268 Background: Checkpoint inhibitor-based immunotherapy has varying success among tumor types and patients. Predictive biomarkers are in high demand to assist with patient selection. Responses to these agents are correlated with programmed cell death ligand 1 (PD-L1) expression, microsatellite instability (MSI), and tumor mutational burden (TMB). Here we evaluated PD-L1 expression, MSI status and TMB in a variety of solid tumors to determine their relationships. Methods: A total of 109 specimens were identified in patients diagnosed with solid tumors that underwent a Paradigm Diagnostic Cancer Test. MSI scores were determined by the number of indels present in runs of 6 homopolymer bases per megabase, with a cut-off of 6 to distinguish between MSI-high (MSI-H) and MSI-stable (MSI-S). TMB scores greater than or equal to 10 muts/Mb were designated as high (TMB-H). Results: Of all tumors, 19.3% were MSI-high. PD-L1 testing was performed in 71.5% of all samples; of these, 21.8% were PD-L1 positive. TMB scores of PD-L1 positive tumors (mean = 10.3 muts/Mb) and PD-L1 negative tumors (mean = 8.6 muts/Mb) did not differ significantly (p = 0.57). All MSI-H tumors were TMB-H, and MSI-H tumors had higher mean TMB scores than MSI-S tumors (57 muts/Mb versus 7.6 muts/Mb; p = 0.06). Among TMB-H tumors, MSI-H status was associated with higher TMB scores compared to MSI-S tumors (55.2 muts/Mb versus 22.1 muts/Mb, p = 0.18). Of the both MSI-S and TMB-H tumors, 68.8% were lung cancers and 18.8% were breast cancers. Tumors both MSI-H and TMB-H were 47.6% gastrointestinal carcinomas and 33.3% endometrial carcinomas. Conclusions: In our analysis, TMB was independent of PD-L1 status. All MSI-H tumors were also TMB-H; these were primarily gastrointestinal and endometrial carcinomas whereas TMB-H tumors that were MSI-S consisted predominantly of lung cancers. This difference may be due to the different mechanisms of acquiring mutations during carcinogenesis in these two tumor types. These data also highlight the need for integrated PD-L1, MSI and TMB testing to identify potential responders to immunotherapy.

Background: Recent studies show tumor mutation burden (TMB) is a biomarker for patient responses to immune checkpoint inhibitors whereas chromosomal instability (CIN) is a marker for PARP inhibitors drug response. Assessment of TMB and CIN in bulk tumor samples is well explored but limited by sample accessibility and tumor heterogeneity. Analysis of ctDNA is challenging for TMB and CIN, especially for patients who harbor subclonal genomic alterations, limiting its clinical utility. Epic Sciences' CTC platform employs a non-enrichment based approach to provide insight into subclonal heterogeneity. Here we present a single cell genomics assay for detection of TMB and CIN from individual CTCs by low pass whole genome sequencing. Methods: Contrived samples were prepared by spiking prostate cancer cell lines LNCaP, PC3 and VCaP into healthy donor blood. Red blood cells were lysed, nucleated cells deposited onto glass slides and immunofluorescently stained (DAPI, CK, CD45 and androgen receptor). Identified cancer cells were individually isolated from the slides, lysed, whole-genome amplified (WGA), shotgun library prepared, and low pass whole genome sequenced to ~ 0.1X coverage. Data were analyzed for TMB and large-scale transitions (LST, a surrogate of CIN). Microsatellite instability (MSI) was measured by the Qiagen Type-It Microsatellite PCR kit as per manufacturer's protocol on the control cell lines. 1,047 CTCs from 108 prostate, breast, colorectal, bladder and lung cancer patients were evaluated for clinical feasibility. Results: TMB scores from LNCaP (average 181) were significantly higher than PC3 (127), VCaP (132), and healthy donor WBC (94). MSI confirmed LNCaP was MSI-H (high) with INDELs detected in 3 of 4 microsatellite sites, whereas PC3 was MSI-L (low) and VCaP was MSS (stable). Although with lower MSI, PC3 (average 33) and VCaP (33) had higher LST scores vs. LNCaP (11). A wide range of TMB (21-480) and LST scores (0-67) was observed in patient samples. TMB cutoff of 175 was set based on cell line TMB scores, and an LST cutoff of 10 was set based on WBC data. Across CTCs from all cancer types and stages, 117 (11.2%) CTCs were TMB-H/LST-L (H: high; L: low), 479 (45.7%) CTCs were TMB-L/LST-H, 31 (3.0%) CTCs were TMB-H/LST-H, and 420 (40.1%) CTCs were TMB-L/LST-L. A significant lower incidence of TMB-H/LST-H CTCs (3.0%) was observed by Fisher's exact test (p<0.0001). Conclusions: These data demonstrate the feasibility of detecting TMB and CIN simultaneously at the single cell level using the Epic Sciences CTC Platform. Inter- and intrapatient heterogeneity was observed in this large patient cohort. Although MSI and HRD are were mutually exclusive driver events in single cells, patients often had both subclonal populations. Studies are ongoing to investigate the potential correlations of TMB with checkpoint inhibitor and CIN with PARPi response. Citation Format: Angel Rodriguez, Jerry Lee, Ramsay Sutton, Rhett Jiles, Gordon Vansant, Yipeng Wang, Mark Landers, Ryan Dittamore. Low-pass whole-genome sequencing of single circulating tumor cells for detection of tumor mutation burden and chromosomal instability across multiple cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3616.

Background: Genomic instability (GI) is a hallmark of cancer often associated with poor patient outcome. TMB, MSI, and CIN represent the majority of GI in metastatic patients. Recent studies show that TMB and MSI are emerging immune checkpoint inhibitor drug sensitivity biomarkers and CIN is a sensitivity marker for PARP inhibitors. Assessment of genomic instability in bulk tumor samples is well explored, but it is limited by sample availability and tumor heterogeneity. Analysis of ctDNA is feasible for TMB and MSI analysis but not CIN, and it also suffers in sensitivity and specificity in patients who harbor subclonal GI, limiting the clinical utility of these assays to detect early clonal alterations. The Epic Sciences CTC platform is a non-enrichment-based approach for the detection and characterization of rare tumor cells in clinical blood samples, and could provide insight into subclonal heterogeneity. Here we present downstream single cell GI assay(s) for the detection of TMB, MSI and CIN from individual CTCs using next-generation sequencing and PCR. Methods: Contrived samples were prepared by spiking three prostate cancer cell lines, LNCaP, PC3, and VCaP, into healthy donor blood. Red blood cells were lysed, all nucleated cells deposited onto slides, slides immunofluorescence stained (DAPI, CK, CD45, and Androgen Receptor), and identified cancer cells individually picked up from the slides. Each recovered cell was lysed, whole genome amplified (WGA), shotgun library prepared, and low pass whole genome sequenced using Illumina NextSeq 500. Data were analyzed for TMB scores (# of INDELs per Mbp) and large-scale transitions (LSTs, a surrogate of CIN). MSI was measured using Qiagen Type-It microsatellite PCR kit for four sites (BAT26, BAT25, D2S123, and D5S346). Samples from metastatic castration resistant prostate cancer (mCRPC) patients were included to evaluate clinical feasibility. Results: TMB scores for LNCaP (average 652, coefficient of variation 15%) were significantly higher than PC3 (558, 0.9%), VCaP (548, 1.1%), and WBC from healthy donor (540, 7.6%) with p<0.01. MSI assay confirmed that LNCaP is the only MSI-H cell with insertions/deletions found in 3 (BAT26, BAT25, and D5S346) of 4 microsatellite sites. No MMR deficiency was found in PC3 and VCaP. LST analysis shows PC3 (average 33) and VCaP (33) have much higher LST scores than LNCaP (11). A wide range of TMB (375-861) and LST scores (0-70) are observed inter- and intra-mCRPC patient samples. Conclusions: The data shown here demonstrate the feasibility of detecting three types of genomic instabilities at the single-cell level using the Epic Sciences CTC Platform. Inter- and intra-patient heterogeneity is observed in the small patient cohort. Additionally, the results confirm that MSI and HRD are likely mutually exclusive driver events driving tumor selection in mCRPC. Further studies are ongoing to investigate the correlation of GI markers with PARPi and I/O checkpoint inhibitor responses. Citation Format: Angel Rodriguez, Jerry Lee, Ramsay Sutton, Rhett Jiles, Yipeng Wang, Mark Landers, Ryan Dittamore. Tumor mutation burden (TMB), microsatellite instability (MSI), and chromosomal instability (CIN) analysis using low pass whole genome sequencing of single circulating tumor cell (CTC) [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A061.

The purpose of this study was to understand the impact of population diversity and geographic variation on tumor mutation burden (TMB) scores across cancers and its implication on stratification of patients for immune checkpoint inhibitor (ICI) therapy. This retrospective study used whole-exome sequencing (WES) to profile 1,233 Indian patients with cancer across 30 different cancer types and to estimate their TMB scores. A WES-based pipeline was adopted, along with an indigenously developed strategy for arriving at true somatic mutations. A robust unsupervised machine learning approach was used to understand the distribution of TMB scores across different populations and within the population. The results of the study showed a biphasic distribution of TMB scores in most cancers, with different threshold scores across cancer types. Patients with cancer in India had higher TMB scores compared with the Caucasian patients. We also observed that the TMB score value at 90th percentile (predicting high efficacy to ICI) was high in four different cancer types (sarcoma, ovary, head and neck, and breast) in the Indian cohort as compared with The Cancer Genome Atlas or public cohort. However, in lung and colorectal cancers, the TMB score distribution was similar between the two population cohorts. The findings of this study indicate that it is crucial to benchmark both cancer-specific and population-specific TMB distributions to establish a TMB threshold for each cancer in various populations. Additional prospective studies on much larger population across different cancers are warranted to validate this observation to become the standard of care.

Simple SummaryBiomarkers for predicting the response to immune checkpoint blockade (ICB) includes programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC), microsatellite instability (MSI), and tumor mutation burden (TMB). This study investigated the relationship of these biomarkers using comprehensive cancer panel assay (CCPA) with >500 genes in 588 advanced cancer patients. The work demonstrates that PD-L1 expression is significantly associated with TMB and MSI score, according to primary tumor origin.Programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC), microsatellite instability (MSI), and tumor mutation burden (TMB) have been proposed as a predictive biomarker to predict response to immune checkpoint blockade (ICB). We aimed to find the relationship of PD-L1 IHC to TMB and MSI using a comprehensive cancer panel assay (CCPA) with >500 genes in advanced cancer patients. CCPA results from 588 archived tissue samples were analyzed for TMB and MSI. In seven samples, whole exome sequencing confirmed TMB with Pearson’s correlation coefficient of 0.972 and all MSI-high cases were validated by pentaplex PCR. Association of TMB and MSI with their corresponding PD-L1 IHC was analyzed. The median TMB value of 588 cases was 8.25 mutations (mut)/Mb (range 0–426.8) with different distributions among the tumor types, with high proportions of high-TMB (>10mut/Mb) in tumors from melanoma, colorectal, gastric, and biliary tract. The TMB values significantly correlated with PD-L1 expression, and this correlation was prominent in gastric and biliary tract cancers. Moreover, the MSI score, the proportion of unstable MSI sites to total assessed MSI sites, showed a significant correlation with the TMB values and PD-L1 scores. This study demonstrates that PD-L1 expression is significantly associated with TMB and MSI score and this correlation depends on the location of the primary tumor.

Hepatocellular carcinoma (HCC) is a lethal malignancy due to its heterogeneity and aggressive behavior. Recently, somatic mutations and tumor cell interactions with the surrounding tumor immune microenvironment (TIME) have been reported to participate in HCC carcinogenesis and predict HCC progression. In this study, we aimed to investigate the association between tumor mutational burden (TMB) and TIME in HCC. Additionally, we sought to identify differentially expressed genes (DEGs) associated with HCC prognosis and progression. The expression, clinical, and mutational data were downloaded from the cancer genome atlas (TCGA) database. The immune infiltration levels and TMB levels of the HCC samples were estimated and the samples were divided into immune cluster (ICR)-1 and 2 based on immune infiltration score and high and low TMB groups based on TMB score. Thereafter, differential gene expression analysis was conducted to identify the DEGs in the ICR1/2 and high/low TMB groups, and the intersecting DEGs were selected. Thereafter, Cox regression analysis was performed on 89 significant DEGs, among which 19 were associated with prognosis. These 19 DEGs were then used to construct a prognostic model based on their expression levels and regression coefficients. Thereafter, we analyzed the DEGs in mutant and wildtype TP53 HCC samples and identified high BCL10 and TRAF3 expression in the mutant TP53 samples. BCL10 and TRAF3 expression was detected by real-time quantitative reverse transcription PCR and immunohistochemistry, and their clinical correlation, biological function, and immune infiltration levels were analyzed by chi-square analyses, Gene Set Enrichment Analysis (GSEA), and "ssGSEA", respectively. The results of our study revealed that immune infiltration level was correlated with TMB and that they synergistically predicted poor prognosis of HCC patients. DEGs enriched in immune-related pathways could serve as indicators of immunotherapy response in HCC. Among these DEGs, BCL10 and TRAF3 were highly expressed in HCC tissues, especially in the mutant TP53 group, and they co-operatively exhibited immunological function, thereby affecting HCC progression and prognosis. In this study, we identified BCL10 and TRAF3 as potential prognostic indicators in HCC patients. Additionally, we found that BCL10 and TRAF3 influence TMB and TIME in HCC patients and can be used for the development of immune-based therapies for improving the long-term survival of HCC patients.

Reprogramming of fatty acid metabolism is a newly-identified hallmark of malignancy. However, no studies have systematically investigated the fatty acid metabolism related-gene set in prostate cancer (PCa). A cohort of 381 patients with gene expression and clinical data from The Cancer Genome Atlas was used as the training set, while another cohort of 90 patients with PCa from GEO (GSE70769) was used as the validation set. Differentially expressed fatty acid metabolism-related genes were subjected to least absolute shrinkage and selection operator (LASSO)-Cox regression to establish a fatty acid metabolism-related risk score. Associations between the risk score and clinical characteristics, immune cell infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) score, and response to chemotherapy were analyzed. Finally, the expression level of genes included in the model was validated using real-time PCR. A prognostic risk model based on five fatty acid metabolism related genes (ALDH1A1, CPT1B, CA2, CROT, and NUDT19) were constructed. Tumors with higher risk score were associated with larger tumor size, lymph node involvement, higher Gleason score, and poorer biochemical recurrence (BCR)-free survival. Furthermore, the high- and low-risk tumors exhibited distinct immune cell infiltration features and immune-related pathway activation. High-risk tumors were associated with favorable response to immunotherapy as indicated by high TMB and low TIDE score, but poor response to bicalutamide and docetaxel chemotherapy. This study established a fatty acid metabolism-related gene signature which was predictive of BCR and response to chemotherapy and immunotherapy, providing a novel therapeutic biomarker for PCa.

This review aims to highlight recent advances in prostate cancer tumor-immune microenvironment research and summarize the state-of-the-art knowledge of immune checkpoint inhibitors in prostate cancer. Immune checkpoint inhibitors are the cornerstone of modern immunotherapy which have shown encouraging results across a spectrum of cancers. However, only limited survival benefit has been seen in patients with prostate cancer. Prostate cancer progression and its response to immunotherapies are strongly influenced by the tumor-immune microenvironment, whose feature can be summarized as low amounts of tumor-specific antigens, low frequency of tumor-infiltrating lymphocytes and high frequency of tumor-associated macrophages. To improve the therapeutic effect of immunotherapies, in recent years, many strategies have been applied, of which the most promising ones include the combination of multiple immunotherapeutic agents, the combination of an immunotherapeutic agent with other modalities in parallel or in sequential, and the development of biomarkers to find a subgroup of patients who may benefit the most from immunotherapeutic agents. The impact of immune content and specific immune cell types on prostate cancer biology is highly complex. Recent clinical trials have shed light on the optimal use of immunotherapies for prostate cancer.

e14265 Background: In the context of immuno-oncology related cancer treatment, tumor mutational burden (TMB) is currently explored as a predictive biomarker for several human malignancies. Several sequencing assays are increasingly commercially available. Established methodologies require rather large amounts of DNA input (in the range of 100 ng) which, however, are frequently not available from small biopsies. We aim to investigate how tissue size and DNA input influence TMB scores. Methods: DNA from 20 specimens (12 biopsies of non-small cell lung cancer (NSCLC); 8 surgical resection specimens from NSCLC, colorectal cancer and endometrial carcinomas) was manually extracted by using the Qiagen GeneRead DNA FFPE kit. Cases were selected to provide a wide range of relative tumor cell content (from < 10% to > 50%) and to include microsatellite-stable and –instable (MSI) cases. Samples were analyzed in triplicates from predefined numbers of unstained sections of a standardized tissue size. DNA quantification was done fluorometrically and by qPCR. Up to 40 ng of DNA were analyzed with the QIASeq TMB Panel (incl. MSI primer boosters; Qiagen). Sequencing was done on an Illumina NextSeq platform, TMB scores and MSI status were determined by using the CLC workbench 5.0.1 (Qiagen). Results: Biopsy samples generated less numbers of DNA molecules (as detected by unique molecular identifiers, UMIs) and less overall reads compared to resection samples. UMI coverage was > 500x in all samples with > 15 ng DNA input. TMB scores were highly reproducible among all samples with > 15 ng DNA but differed significantly among samples with limited DNA input. Interestingly, TMB scores were inversely correlated with DNA input among those samples with < 15ng. Thus, valid TMB scores could also be obtained from only one slice of 1 cm2 tissue from tumor resections or 3 slices of an endoscopic biopsy (each of 5µm thickness). All pre-characterized MSI carcinomas could be detected correctly. Conclusions: We provide first evidence that TMB can be reliably determined also in small biopsies yielding limited DNA content. However, false high TMB scores can occur in samples with < 15ng DNA input. Our results contribute to the definition of minimum requirements (tissue size and DNA input) for valid TMB measurement on clinical FFPE samples.

Background Prostate cancer (PC) is one of the most common malignancies in males. Extensive and complex connections between circadian rhythm and cancer were found. Nonetheless, in PC, the potential role of the core components of the mammalian circadian clock (CCMCCs) in prognosis prediction has not been fully clarified. Methods We firstly collected 605 patients with PC from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Survival analysis was carried out for each CCMCC. Then, we investigated the prognostic ability of CCMCCs by Cox regression analysis. Independent prognostic signatures were extracted for the establishment of the circadian clock-based risk score model. We explored the predictive performance of the risk score model in the TCGA training cohort and the independent GEO dataset. Finally, the relationships between risk score and clinicopathological parameters, biological processes, and signaling pathways were evaluated. Results The expression levels of CCMCCs were widely correlated with age, tumor status, lymph node status, disease-free survival (DFS), progression-free survival (PFS), and overall survival (OS). Nine circadian clock genes, including CSNK1D, BTRC, CLOCK, CSNK1E, FBXL3, PRKAA2, DBP, NR1D2, and RORB, were identified as vital prognostic factors in PC and were used to construct the circadian clock-based risk score model. For DFS, the area under the 3-year or 5-year receiver operating characteristic curves ranged from 0.728 to 0.821, suggesting better predictive performance. When compared with T3-4N1 stage, PC patients at T2N0 stage might be benefited more from the circadian clock-based risk score model. Furthermore, a high circadian clock-based risk score indicated shorter DFS (p < 0.0001), early progression (p < 0.0001), and higher 5-year death rate (p = 0.007) in PC. The risk score was related to tumor status (p < 0.001), lymph node status (p < 0.001), and ribosome-related biogenesis and pathways. Conclusions The vital roles of circadian clock genes in clinical outcomes were fully depicted. The circadian clock-based risk score model could reflect and predict the prognosis of patients with PC.

We developed an immune-related gene prognostic index (IGPI) associated with progression and provided new insights into the tumour immune microenvironment (TIME) for prostate cancer (PCA) patients undergoing radical prostatectomy. All analyses were conducted with R software (version 3.6.3) and its suitable packages. Meta-analysis was performed with STATA 16.0. TUBB3, WDR62 and PPARGC1A were finally identified to establish the IGPI score. The IGPI score increased with the augment of the Gleason score and T stage, as well as biochemical recurrence (BCR) and prostate specific antigen (PSA). Patients with a higher IGPI score were at a higher risk of progress (HR: 2·88; 95%CI: 95%CI: 1·80-4·61). Gene set enrichment analysis indicated that patients in high-risk group were positively associated with mismatch repair, cell cycle, DNA replication, base excision repair, nucleotide excision repair, homologous recombination and pyrimidine metabolism. We observed that patients in the high-risk group had significantly higher tumour mutation burden score and microsatellite instability score than those in the low-risk group. For analysis of immune checkpoint, ADORA2A, CD80, TNFRSF4, TNFRSF18 and TNFRSF25 were differentially expressed between no progress and progress groups and were significantly associated with progress free survival. We observed positive correlations between the IGPI score and lymphoid immune cells, macrophages M2 and immune score, while negative association between the IGPI score and dendritic cells, fibroblasts, stromal score and microenvironment score. In conclusion, the IGPI score constructed in this study might serve as an independent risk factor associated with PCA progression. ADORA2A, CD80, TNFRSF4, TNFRSF18 and TNFRSF25might be the potential targets in the treatment of PCA.

148 Background: Tumor mutational burden (TMB) measured via next-generation sequencing (NGS)-based gene panel testing is a promising biomarker for response to immune checkpoint inhibitors (ICIs) in solid tumors. However, little is known about the characteristics of tissue sample that can affect the TMB score. Methods: Data of 199 patients with solid tumors who underwent NGS-based gene panel assay were reviewed. Associations between the TMB score and sample characteristics, including sample DNA quality, sample type, sampling site, and storage period, were analyzed. Clinical outcomes of patients with a high TMB score (≥10 mutations/megabase) who received anti-PD-1 antibodies (n = 22) were also analyzed. Results: TMB score was significantly higher in samples that yielded a DNA library concentration of &lt; 5 nM and in samples obtained from formalin-fixed paraffin-embedded tissue (FFPE) compared to fresh frozen tissue. The sample storage period was also positively correlated with TMB score. Of the 22 patients with a high TMB score, partial response and stable disease were observed in two (9.1%) and five (22.7%) cases, respectively. Among 15 non-responders, 11 (73.3%) had a low DNA library concentration, thereby indicating that a high TMB score might be attributable to false positive mutation calls. Conclusions: Our results suggest that the TMB score estimated via NGS-based gene panel assay could be affected by the DNA library concentration, sample type and storage period.