Improving the turnaround time of molecular profiling for advanced non-small cell lung cancer: Outcome of a new algorithm integrating multiple approaches.

BACKGROUND: Molecular tumor profiling to identify oncogenic drivers and actionable mutations has a profound impact on how lung cancer is treated. Especially in the subgroup of non-small cell lung cancer (NSCLC), molecular testing for certain mutations is crucial in daily clinical practice and is recommended by international guidelines. To date, a standardized approach to identify druggable genetic alterations are lacking. We have developed and implemented a new diagnostic algorithm to harmonize the molecular testing of NSCLC. PATIENTS AND METHODS: In this retrospective analysis, we reviewed 119 patients diagnosed with NSCLC at the University Hospital Zurich. Tumor samples were analyzed using our standardized diagnostic algorithm: After the histological diagnosis was made, tissue samples were further analyzed by immunohistochemical stainings as well as the real-time PCR test Idylla™. Extracted DNA was further utilized for comprehensive genomic profiling (FoundationOne®CDx, F1CDx). RESULTS: Out of the 119 patients were included in this study, 100 patients were diagnosed with non-squamous NSCLC (nsqNSCLC) and 19 with squamous NSCLC (sqNSCLC). The samples from the nsqNSCLC patients underwent testing by Idylla™ and were evaluated by immunohistochemistry (IHC). F1CDx analysis was run on 67 samples and 46 potentially actionable genomic alterations were detected. Ten patients received the indicated targeted treatment. The median time to test results was 4 days for the Idylla test, 5 days for IHC and 13 days for the F1CDx. CONCLUSION: In patients with NSCLC, the implementation of a standardized molecular testing algorithm provided information on predictive markers for NSCLC within a few working days. The implementation of broader genomic profiling led to the identification of actionable targets, which would otherwise not have been discovered.

The Role of the Liquid Biopsy in Decision-Making for Patients with Non-Small Cell Lung Cancer.

Liquid biopsy is a rapidly emerging tool of precision oncology enabling minimally invasive molecular diagnostics and longitudinal monitoring of treatment response. For the clinical management of advanced stage lung cancer patients, detection and quantification of circulating tumor DNA (ctDNA) is now widely adopted into clinical practice. Still, interpretation of results and validation of ctDNA-based treatment decisions remain challenging. We report here our experience implementing liquid biopsies into the clinical management of lung cancer. We discuss advantages and limitations of distinct ctDNA assay techniques and highlight our approach to the analysis of recurrent molecular alterations found in lung cancer. Moreover, we report three exemplary clinical cases illustrating the complexity of interpreting liquid biopsy results in clinical practice. These cases underscore the potential and current limitations of liquid biopsy, focusing on the difficulty of interpreting discordant findings. In our view, despite all current limitations, the analysis of ctDNA in lung cancer patients is an essential and highly versatile complementary diagnostic tool for the clinical management of lung cancer patients in the era of precision oncology.

An internally validated new clinical and inflammation-based prognostic score for patients with advanced hepatocellular carcinoma treated with sorafenib

Background. Sorafenib is a standard treatment for patients (pts) with advanced hepatocellular carcinoma (aHCC), although the clinical benefit is heterogeneous between different pts groups. Among novel prognostic factors, a low baseline neutrophil-to-lymphocyte ratio (bNLR) and early-onset diarrhoea have been linked with a better prognosis. Purpose. To identify prognostic factors in pts with aHCC treated with 1st-line sorafenib and to develop a new prognostic score to guide management. Materials and methods. Retrospective review of 145 pts bNLR, overall toxicity, early toxicity rates and overall survival (OS) were assessed. Univariate and multivariate analysis of prognostic factors for OS was performed. The prognostic score was calculated from the coefficients found in the Cox analysis. ROC curves and pseudoR2 index were used for internal validation. Discrimination ability and calibration were tested by Harrel’s c-index (HCI) and Akaike criteria (AIC). Results. The optimal bNLR cut-off for the prediction of OS was 4 (AUC 0.62). Independent prognostic factors in multivariate analysis for OS were performance status (PS) (p < .0001), Child-Pugh (C-P) score (p = 0.005), early-onset diarrhoea (p = 0.006) and BNLR (0.011). The prognostic score based on these four variables was found efficient (HCI = 0.659; AIC = 1.180). Four risk groups for OS could be identified: a very low-risk (median OS = 48.6 months), a low-risk (median OS = 11.6 months), an intermediate-risk (median OS = 8.3 months) and a high-risk group (median OS = 4.4 months). Conclusions. PS and C-P score were the main prognostic factors for OS, followed by early-onset diarrhoea and bNLR. We identified four risk groups for OS depending on these parameters. This prognostic model could be useful for patient stratification, but an external validation is needed (AU)

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An internally validated new clinical and inflammation-based prognostic score for patients with advanced hepatocellular carcinoma treated with sorafenib.

BACKGROUND: Sorafenib is a standard treatment for patients (pts) with advanced hepatocellular carcinoma (aHCC), although the clinical benefit is heterogeneous between different pts groups. Among novel prognostic factors, a low baseline neutrophil-to-lymphocyte ratio (bNLR) and early-onset diarrhoea have been linked with a better prognosis. PURPOSE: To identify prognostic factors in pts with aHCC treated with 1st-line sorafenib and to develop a new prognostic score to guide management. MATERIALS AND METHODS: Retrospective review of 145 pts bNLR, overall toxicity, early toxicity rates and overall survival (OS) were assessed. Univariate and multivariate analysis of prognostic factors for OS was performed. The prognostic score was calculated from the coefficients found in the Cox analysis. ROC curves and pseudoR2 index were used for internal validation. Discrimination ability and calibration were tested by Harrel's c-index (HCI) and Akaike criteria (AIC). RESULTS: The optimal bNLR cut-off for the prediction of OS was 4 (AUC 0.62). Independent prognostic factors in multivariate analysis for OS were performance status (PS) (p < .0001), Child-Pugh (C-P) score (p = 0.005), early-onset diarrhoea (p = 0.006) and BNLR (0.011). The prognostic score based on these four variables was found efficient (HCI = 0.659; AIC = 1.180). Four risk groups for OS could be identified: a very low-risk (median OS = 48.6 months), a low-risk (median OS = 11.6 months), an intermediate-risk (median OS = 8.3 months) and a high-risk group (median OS = 4.4 months). CONCLUSIONS: PS and C-P score were the main prognostic factors for OS, followed by early-onset diarrhoea and bNLR. We identified four risk groups for OS depending on these parameters. This prognostic model could be useful for patient stratification, but an external validation is needed.