Diagnostic performance of early increase in S100B or LDH as outcome predictor for non-responsiveness to anti-PD-1 monotherapy in advanced melanoma.

As a subset of advanced melanoma patients derive long-term benefit from anti-PD-1 therapy, early identification of non-responsiveness would enable an early switch to next line therapies. This study assessed if an early increase in S100B or lactate dehydrogenase (LDH) could be predictive for non-responsiveness to anti-PD-1. We retrospectively analysed advanced melanoma patients treated with anti-PD-1 monotherapy. Serum S100B and LDH levels were measured at baseline and before every infusion. Non-response was defined as progression or death at 6 months. Marker cut-offs were defined based on > 95% specificity and feasibility in clinical practice. For validation an independent cohort was analysed. In total, 313 patients were included (166 patients in training cohort, 147 patients in validation cohort). Increase of > 50% in LDH or > 100% in S100B above upper limit of normal at week 6 compared to baseline was determined as criterion to positively test for non-responsiveness. In the validation cohort, obtained specificity of the combination test was > 95% with a positive predictive value of 82%; obtained sensitivity was lower (21%), with a negative predictive value of 55%. Early increase in S100B or LDH is a strong parameter for non-responsiveness to anti-PD-1 in advanced melanoma. Prospective confirmation is needed before clinical implementation.

Validation of a clinical blood-based decision aid to guide immunotherapy treatment in patients with non-small cell lung cancer.

BACKGROUND: The widespread introduction of immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has led to durable responses but still many patients fail and are treated beyond progression. OBJECTIVE: This study investigated whether readily available blood-based tumor biomarkers allow accurate detection of early non-responsiveness, allowing a timely switch of therapy and cost reduction. METHODS: In a prospective, observational study in patients with NSCLC treated with nivolumab or pembrolizumab, five serum tumor markers were measured at baseline and every other week. Six months disease control as determined by RECIST was used as a measure of clinical response. Patients with a disease control <  6 months were deemed non-responsive. For every separate tumor marker a criterion for predicting of non-response was developed. Each marker test was defined as positive (predictive of non-response) if the value of that tumor marker increased at least 50% from the value at baseline and above a marker dependent minimum value to be determined. Also, tests based on combination of multiple markers were designed. Specificity and sensitivity for predicting non-response was calculated and results were validated in an independent cohort. The target specificity of the test for detecting non-response was set at >  95%, in order to allow its safe use for treatment decisions. RESULTS: A total of 376 patients (training cohort: 180, validation cohort: 196) were included in our analysis. Results for the specificity of the single marker tests in the validation set were CEA: 98·3% (95% CI: 90·9-100%), NSE: 96·5% (95% CI: 87·9-99·6%), SCC: 96·5% (95% CI: 88·1-99·6%), Cyfra21·1 : 91.8% (95% CI: 81·9-97·3%), and CA125 : 86·0% (95% CI: 74·2-93·7%). A test based on the combination of Cyfra21.1, CEA and NSE accurately predicted non-response in 32.3% (95% CI 22.6-43.1%) of patients 6 weeks after start of immunotherapy. Survival analysis showed a significant difference between predicted responders (Median PFS: 237 days (95% CI 184-289 days)) and non-responders (Median PFS: 58 days (95% CI 46-70 days)) (p <  0.001). CONCLUSIONS: Serum tumor marker based tests can be used for accurate detection of non-response in NSCLC, thereby allowing early and safe discontinuation of immunotherapy in a significant subset of patients.

Dutch genome diagnostic laboratories accelerated and improved variant interpretation and increased accuracy by sharing data.

Each year diagnostic laboratories in the Netherlands profile thousands of individuals for heritable disease using next-generation sequencing (NGS). This requires pathogenicity classification of millions of DNA variants on the standard 5-tier scale. To reduce time spent on data interpretation and increase data quality and reliability, the nine Dutch labs decided to publicly share their classifications. Variant classifications of nearly 100,000 unique variants were catalogued and compared in a centralized MOLGENIS database. Variants classified by more than one center were labeled as "consensus" when classifications agreed, and shared internationally with LOVD and ClinVar. When classifications opposed (LB/B vs. LP/P), they were labeled "conflicting", while other nonconsensus observations were labeled "no consensus". We assessed our classifications using the InterVar software to compare to ACMG 2015 guidelines, showing 99.7% overall consistency with only 0.3% discrepancies. Differences in classifications between Dutch labs or between Dutch labs and ACMG were mainly present in genes with low penetrance or for late onset disorders and highlight limitations of the current 5-tier classification system. The data sharing boosted the quality of DNA diagnostics in Dutch labs, an initiative we hope will be followed internationally. Recently, a positive match with a case from outside our consortium resulted in a more definite disease diagnosis.