High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot solid-phaseenhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96-well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands-on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low-input samples, reproducibly quantifying 500-1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end-to-end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost-effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non-clinical applications.
Background: Panel sequencing based estimates of tumor mutational burden (psTMB) are increasingly replacing whole exome sequencing (WES) tumor mutational burden as predictive biomarker of immune checkpoint blockade (ICB).Design: A mathematical law describing psTMB variability was derived using a random mutation model and complemented by the contributions of non-randomly mutated real-world cancer genomes and intratumoral heterogeneity through simulations in publicly available datasets.Results: The coefficient of variation (CV) of psTMB decreased inversely proportional with the square root of the panel size and the square root of the TMB level. In silico simulations of all major commercially available panels in the TCGA pan-cancer cohort confirmed the validity of this mathematical law and demonstrated that the CV was 35% for TMB ¼ 10 muts/Mbp for the largest panels of size 1.1-1.4 Mbp. Accordingly, misclassification rates (gold standard: WES) to separate 'TMBhigh' from 'TMBlow' using a cut-point of 199 mutations were 10%-12% in TCGA-LUAD and 17%-19% in TCGA-LUSC. A novel three-tier psTMB classification scheme which accounts for the likelihood of misclassification is proposed. Simulations in two WES datasets of immunotherapy treated patients revealed that small gene panels were poor predictors of ICB response. Moreover, we noted substantial intratumoral variance of psTMB scores in the TRACERx 100 cohort and identified indel burden as independent marker complementing missense mutation burden.Conclusions: A universal mathematical law describes accuracy limitations inherent to psTMB, which result in substantial misclassification rates. This scenario can be controlled by two measures: (i) a panel design that is based on the mathematical law described in this article: halving the CV requires a fourfold increase in panel size, (ii) a novel three-tier TMB classification scheme. Moreover, inclusion of indel burden can complement TMB reports. This work has substantial implications for panel design, TMB testing, clinical trials and patient management.
INTRODUCTION Tumor mutational burden (TMB) is a quantitative assessment of the number of somatic mutations within a tumor genome. Immunotherapy benefit has been associated with TMB assessed by whole exome sequencing (wesTMB) and by gene panel sequencing (psTMB). The initiatives of Quality in Pathology (QuIP) and Friends of Cancer Research (FoCR) have jointly addressed the need for harmonization between TMB testing options in tissues. This QuIP study identifies critical sources of variation in psTMB assessment. METHODS Twenty samples from three tumor types (LUAD, HNSC, COAD) with available WES data were analyzed for psTMB, using six panels across 15 testing centers. Inter-laboratory and inter-platform variation including agreement on variant calling and TMB classification were investigated. Bridging factors to transform psTMB to wesTMB values were empirically derived. The impact of germline filtering was evaluated. RESULTS Sixteen samples demonstrated low interlaboratory and interpanel psTMB variation with 87.7% of pairwise comparisons showing a Spearman's >0.6. A wesTMB cutpoint of 199 missense mutations projected to psTMB cutpoints between 7.8 and 12.6 muts/Mbp; the corresponding psTMB and wesTMB classifications agreed in 74.9% of cases. For three-tier classification with cutpoints of 100 and 300 mutations, agreement was observed in 76.7%, weak misclassification in 21.8%, and strong misclassification in 1.5% of cases. Confounders of psTMB estimation included fixation artifacts, DNA input, sequencing depth, genome coverage, and variant allele frequency cutpoints. CONCLUSIONS This study provides real-world evidence that all evaluated panels can be used to estimate TMB in a routine diagnostic setting and identifies important parameters for reliable tissue TMB assessment that require careful control. As complex/composite biomarkers beyond TMB are likely playing an increasing role in therapy prediction, the efforts by QuIP and FoCR also delineate a general framework and blueprint for the evaluation of such assays.
Next-generation sequencing has become a cornerstone of therapy guidance in cancer precision medicine and an indispensable research tool in translational oncology. Its rapidly increasing use during the last decade has expanded the options for targeted tumor therapies, and molecular tumor boards have grown accordingly. However, with increasing detection of genetic alterations, their interpretation has become more complex and error-prone, potentially introducing biases and reducing benefits in clinical practice. To facilitate interdisciplinary discussions of genetic alterations for treatment stratification between pathologists, oncologists, bioinformaticians, genetic counselors and medical scientists in specialized molecular tumor boards, several systems for the classification of variants detected by large-scale sequencing have been proposed. We review three recent and commonly applied classifications and discuss their individual strengths and weaknesses. Comparison of the classifications underlines the need for a clinically useful and universally applicable variant reporting system, which will be instrumental for efficient decision making based on sequencing analysis in oncology. Integrating these data, we propose a generalizable classification concept featuring a conservative and a more progressive scheme, which can be readily applied in a clinical setting. What's new?With increasingly comprehensive molecular profiling of cancers, the clinical interpretation of genetic alterations is becoming more and more challenging. Here the authors review several classifications for gene variant interpretation that were recently introduced to guide clinical management. They highlight shared features and differences and point out major influencing factors and unresolved issues that still need to be addressed. Based on this analysis, they propose a unified classification concept that may become broadly implemented when remaining issues are solved.
Prostate-specific membrane antigen (PSMA) targeting alpha-radiation therapy (TAT) is an emerging treatment modality for metastatic castration-resistant prostate cancer. There is a subgroup of patients with poor response despite sufficient expression of PSMA in their tumors. The aim of this work was to characterize PSMA-TAT nonresponding lesions by targeted next-generation sequencing (tNGS). Methods: Out of 60 patients treated with 225 Ac-PSMA-617, we identified 10 patients that presented with a poor response despite sufficient tumor-uptake in PSMA-PET/CT. We were able to perform CT-guided biopsies with histologic validation of the non-responding lesions in seven of these non-responding patients. Specimens were analyzed by tNGS interrogating 37 DNA damage-repair associated genes. Results: In the seven tumor samples analyzed, we found a total of 15 whole-gene deletions, deleterious or presumably deleterious mutations affecting TP53 (n=3); CHEK2 (n=2), ATM (n=2); BRCA1, BRCA2, PALB2, MSH2, MSH6, NBN, FANCB and PMS1 (n=1 each). The average number of deleterious or presumably deleterious mutations was 2.2 (range, 0-6) per patient. In addition, several variants of unknown significance in ATM, BRCA1, MSH2, SLX4, ERCC-and various FANC-genes were detected. Conclusion: Patients with resistance to PSMA-TAT despite PSMA-positivity frequently harbor mutations in DNA-damage-repair and checkpoint genes. While the causal role of these alterations in the patient outcome remains to be determined, our findings encourage future studies combining PSMA-TAT and DNA-damage-repair targeting agents such as Poly(ADPribose)-Polymerase inhibitors.
Objective: Recognition of neuroendocrine differentiation is important for tumor classification and treatment stratification. To detect and confirm neuroendocrine differentiation, a combination of morphology and immunohistochemistry is often required. In this regard, synaptophysin, chromogranin A, and CD56 are established immunohistochemical markers. Insulinoma-associated protein 1 (INSM1) has been suggested as a novel stand-alone marker with the potential to replace the current standard panel. In this study, we compared the sensitivity and specificity of INSM1 and established markers. Materials and Methods: A cohort of 493 lung tumors including 112 typical, 39 atypical carcinoids, 77 large cell neuroendocrine carcinomas, 144 small cell lung cancers, 30 thoracic paragangliomas, 47 adenocarcinomas, and 44 squamous cell carcinomas were selected and tissue microarrays were constructed. Synaptophysin, chromogranin A, CD56, and INSM1 were stained on all cases and evaluated manually as well as with an analysis software. Positivity was defined as ≥1% stained tumor cells in at least 1 of 2 cores per patient. Results: INSM1 was positive in 305 of 402 tumors with expected neuroendocrine differentiation (typical and atypical carcinoids, large cell neuroendocrine carcinomas, small cell lung cancers, and paraganglioma; sensitivity: 76%). INSM1 was negative in all but 1 of 91 analyzed non-neuroendocrine tumors (adenocarcinomas, squamous cell carcinomas; specificity: 99%). All conventional markers, as well as their combination, had a higher sensitivity (97%) and a lower specificity (78%) for neuroendocrine differentiation compared with INSM1. Conclusions: Although INSM1 might be a meaningful adjunct in the differential diagnosis of neuroendocrine neoplasias, a general uncritical vote for replacing the traditional markers by INSM1 may not be justified.
Background: Tumor mutational burden (TMB) is an emerging biomarker used to identify patients who are more likely to benefit from immuno-oncology therapy. Aside from various unsettled technical aspects, biological variables such as tumor cell content and intratumor heterogeneity may play an important role in determining TMB. Methods: TMB estimates were determined applying the TruSight Oncology 500 targeted sequencing panel. Spatial and temporal heterogeneity was analyzed by multiregion sequencing (two to six samples) of 24 pulmonary adenocarcinomas and by sequencing a set of matched primary tumors, locoregional lymph node metastases, and distant metastases in five patients. Results: On average, a coding region of 1.28 Mbp was covered with a mean read depth of 609x. Manual validation of the mutation-calls confirmed a good performance, but revealed noticeable misclassification during germline filtering. Different regions within a tumor showed considerable spatial TMB variance in 30% (7 of 24) of the cases (maximum difference, 14.13 mut/Mbp). Lymph node-derived TMB was significantly lower (p ¼ 0.016). In 13 cases, distinct mutational profiles were exclusive to different regions of a tumor, leading to higher values for simulated aggregated TMB. Combined, intratumor heterogeneity and the aggregated TMB could result in divergent TMB designation in 17% of the analyzed patients. TMB variation between primary tumor and distant metastases existed but was not profound. Conclusions: Our data show that, in addition to technical aspects such as germline filtering, the tumor content and spatially divergent mutational profiles within a tumor are relevant factors influencing TMB estimation, revealing limitations of singlesample-based TMB estimations in a clinical context.
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