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Objectives Owing to novel therapy strategies in epidermal growth factor receptor (EGFR)-mutated patients, molecular analysis of the EGFR and KRAS genome has become crucial for routine diagnostics. Till date these data have been derived mostly from clinical trials, and thus collected in pre-selected populations. We therefore screened ‘allcomers’ with a newly diagnosed non-small cell lung carcinoma (NSCLC) for the frequencies of these mutations. Design A cohort study. Setting Lung cancer centre in a tertiary care hospital. Participants Within 15 months, a total of 552 cases with NSCLC were eligible for analysis. Primary and secondary outcome measures Frequency of scrutinising exons 18, 19 and 21 for the presence of activating EGFR mutation and secondary codon 12 and 13 for activating KRAS mutations. Results Of the 552 patients, 27 (4.9%) showed a mutation of EGFR. 19 of these patients (70%) had deletion E746-A750 in codon 19 or deletion L858R in codon 21. Adenocarcinoma (ACA) was the most frequent histology among patients with EGFR mutations (ACA, 22/254 (8.7%) vs non-ACA, 5/298 (1.7%); p<0.001). Regarding only ACA, the percentage of EGFR mutations was higher in women (16/116 (14%) women vs 6/138 (4.3%) men; p=0.008). Tumours with an activating EGFR mutation were more likely to be from non-smokers (18/27; 67%) rather than smoker (9/27; 33%). KRAS mutation was present in 85 (15%) of all cases. In 73 patients (86%), the mutation was found in exon 12 and in 12 cases (14%) in exon 13. Similarly, ACA had a higher frequency of KRAS mutations than non-ACA (67/254 (26%) vs 18/298 (6.0%); p<0.001). Conclusions We found a lower frequency for EGFR and KRAS mutations in an unselected Caucasian patient cohort as previously published. Taking our results into account, clinical trials may overestimate the mutation frequency for EGFR and KRAS in NSCLC due to important selection biases.

Background Immune checkpoint inhibitors (ICIs) are currently one of the most promising therapy options in the field of oncology. Although the first pivotal ICI trial results were published in 2011, few biomarkers exist to predict their therapy outcome. PD-L1 expression and tumor mutational burden (TMB) were proven to be sometimes-unreliable biomarkers. We have previously suggested the analysis of processing escapes, a qualitative measurement of epitope structure alterations under immune system pressure, to provide predictive information on ICI response. Here, we sought to further validate this approach and characterize interactions with different forms of immune pressure. Methods We identified a cohort consisting of 48 patients with advanced non-small cell lung cancer (NSCLC) treated with nivolumab as ICI monotherapy. Tumor samples were subjected to targeted amplicon-based sequencing using a panel of 22 cancer-associated genes covering 98 mutational hotspots. Altered antigen processing was predicted by NetChop, and MHC binding verified by NetMHC. The NanoString nCounter® platform was utilized to provide gene expression data of 770 immune-related genes. Patient data from 408 patients with NSCLC were retrieved from The Cancer Genome Atlas (TCGA) as a validation cohort. Results The two immune escape mechanisms of PD-L1 expression (TPS score) ( n  = 18) and presence of altered antigen processing ( n  = 10) are mutually non-exclusive and can occur in the same patient ( n  = 6). Both mechanisms have exclusive influence on different genes and pathways, according to differential gene expression analysis and gene set enrichment analysis, respectively. Interestingly, gene expression patterns associated with altered processing were enriched in T cell and NK cell immune activity. Though both mechanisms influence different genes, they are similarly linked to increased immune activity. Conclusion Pressure from the immune system will lay the foundations for escape mechanisms, leading to acquisition of resistance under therapy. Both PD-L1 expression and altered antigen processing are induced similarly by pronounced immunoactivity but in different context. The present data help to deepen our understanding of the underlying mechanisms behind those immune escapes.

Objective Calretinin is a well-known immunohistochemical tissue marker in the diagnosis of malignant mesothelioma. Promising results also indicate the use in early detection. In the present cross-sectional survey, correlations of calretinin plasma levels with clinical features were investigated. Plasma samples of 60 patients with malignant pleural mesothelioma (MPM) and 111 cancer-free controls formerly exposed to asbestos were compared. Calretinin concentrations were determined in plasma using an enzyme-linked immunosorbent assay (ELISA). Results The median concentration was higher in MPM patients than in controls (0.79 vs. 0.23 ng/ml; p  < 0.0001). Patients with epithelioid MPM or biphasic MPM had higher calretinin plasma levels than patients with sarcomatoid MPM. Strong expression of calretinin in the tumor tissue was associated with higher plasma levels. Preoperative patients showed higher levels of calretinin than patients after thoracic surgery (1.20 vs. 0.67 ng/ml; p  = 0.096). The suitability of plasma calretinin has been confirmed as a tumor marker in the differential diagnosis of epithelioid MPM. The value of plasma calretinin for therapy monitoring or as a prognostic marker should be further investigated.

Pulmonary sarcomatoid carcinoma (PSC) has highly aggressive biological behaviour and poor clinical outcomes, raising expectations for new therapeutic strategies. We characterized 179 PSC by immunohistochemistry, next-generation sequencing and in silico analysis using a deep learning algorithm with respect to clinical, immunological and molecular features. PSC was more common in men, older ages and smokers. Surgery was an independent factor (p < 0.01) of overall survival (OS). PD-L1 expression was detected in 82.1% of all patients. PSC patients displaying altered epitopes due to processing mutations showed another PD-L1-independent immune escape mechanism, which also significantly influenced OS (p < 0.02). The effect was also maintained when only advanced tumour stages were considered (p < 0.01). These patients also showed improved survival with a significant correlation for immunotherapy (p < 0.05) when few or no processing mutations were detected, although this should be interpreted with caution due to the small number of patients studied. Genomic alterations for which there are already approved drugs were present in 35.4% of patients. Met exon 14 skipping was found more frequently (13.7%) and EGFR mutations less frequently (1.7%) than in other NSCLC. In summary, in addition to the divergent genomic landscape of PSC, the specific immunological features of this prognostically poor subtype should be considered in therapy stratification.

Precision oncology and immunotherapy have revolutionized the treatment of advanced non-small-cell lung cancer (NSCLC). Emerging studies show that targeted therapies are also beneficial for patients with driver alterations such as epidermal growth factor receptor (EGFR) mutations in early-stage NSCLC (stages I–IIIA). Furthermore, patients with elevated programmed death-ligand 1 (PD-L1) expression appear to respond favorably to adjuvant immunotherapy. To determine the frequency of genomic alterations and PD-L1 status in early-stage NSCLC, we retrospectively analyzed data from 2066 unselected, single-center patients with NSCLC diagnosed using next-generation sequencing and immunohistochemistry. Nine-hundred and sixty-two patients (46.9%) presented with early-stage NSCLC. Of these, 37.0% had genomic alterations for which targeted therapies have already been approved for advanced NSCLC. The frequencies of driver mutations in the early stages were equivalent to those in advanced stages, i.e., the rates of EGFR mutations in adenocarcinomas were 12.7% (72/567) and 12.0% (78/650) in early and advanced NSCLC, respectively (p = 0778). In addition, 46.3% of early-stage NSCLC cases were PD-L1-positive, with a tumor proportion score (TPS) of ≥1%. With comparable frequencies of driver mutations in early and advanced NSCLC and PD-L1 overexpression in nearly half of patients with early-stage NSCLC, a broad spectrum of biomarkers for adjuvant and neoadjuvant therapies is available, and several are currently being investigated in clinical trials.

Combined hepatocellular cholangiocarcinoma (cHCC-CC) is a rare primary liver malignancy that comprises features of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). Due to the rarity of this tumor, the treatment of choice has not yet been defined. For resectable disease, liver resection is the mainstay treatment. However, most patients relapse or display advanced disease and were not surgical candidates. Although the majority of patients are either primarily or secondarily treated in palliative intent, no guideline recommendations or prospective trial reports exist to allow reliable evaluation of debated treatment options. We review different locoregional or medical treatment options for advanced combined hepatocellular cholangiocarcinoma (cHCC-CC) in the neoadjuvant, adjuvant, or palliative setting and discuss the possibility of predictive biomarker-guided therapeutic options.

Immune checkpoint inhibition, especially the blockade of PD-1 and PD-L1, has become one of the most thriving therapeutic approaches in modern oncology. Immune evasion caused by altered tumor epitope processing (so-called processing escapes) may be one way to explain immune checkpoint inhibition therapy failure. In the present study, we aim to demonstrate the effects of processing escapes on immunotherapy outcome in NSCLC patients. Whole exome sequencing data of 400 NSCLC patients (AdC and SCC) were extracted from the TCGA database. The ICB cohort was composed of primary tumor probes from 48 NSCLC patients treated with nivolumab. Mutations were identified by targeted amplicon-based sequencing including hotspots and whole exomes of 22 genes. The effect of mutations on proteasomal processing was evaluated by deep learning methods previously trained on 1260 known MHC-I ligands. Cox regression modelling was used to determine the influence on overall survival. In the TCGA cohort, processing escapes were associated with decreased overall survival (p= 0.0140). In the ICB cohort, patients showing processing escapes in combination with high levels of PD-L1 (n=8/48) also showed significantly decreased overall survival, independently of mutational load or PD-L1 status. The concept of altered epitope processing may help to understand immunotherapy failure. Especially when combined with PD-L1 status, this method can be used as a biomarker to identify patients not suitable for immunotherapy.

Background: Immune checkpoint inhibition, especially the blockade of PD-1 and PD-L1, has become one of the most thriving therapeutic approaches in modern oncology. Immune evasion caused by altered tumor epitope processing (so-called processing escapes) may be one way to explain immune checkpoint inhibition therapy failure. In the present study, we aim to demonstrate the effects of processing escapes on immunotherapy outcome in NSCLC patients. Patients and Methods: Whole exome sequencing data of 400 NSCLC patients (AdC and SCC) were extracted from the TCGA database. The ICB cohort was composed of primary tumor probes from 48 NSCLC patients treated with nivolumab. Mutations were identified by targeted amplicon-based sequencing including hotspots and whole exomes of 22 genes. The effect of mutations on proteasomal processing was evaluated by deep learning methods previously trained on 1260 known MHC-I ligands. Cox regression modelling was used to determine the influence on overall survival. Results: In the TCGA cohort, processing escapes were associated with decreased overall survival (p= 0.0140). In the ICB cohort, patients showing processing escapes in combination with high levels of PD-L1 (n=8/48) also showed significantly decreased overall survival, independently of mutational load or PD-L1 status. Conclusion: The concept of altered epitope processing may help to understand immunotherapy failure. Especially when combined with PD-L1 status, this method can be used as a biomarker to identify patients not suitable for immunotherapy. Keywords: massive parallel sequencing, NSCLC, immunotherapy, epitope, processing escape, deep learning