A novel algorithm for reliable detection of human papillomavirus in paraffin embedded head and neck cancer specimen

Human papillomavirus type 16 (HPV16) plays a role in the development of a subgroup of head and neck squamous cell carcinomas (HNSCC). However, uncertainty exists about the true impact of HPV in this tumor type as conflicting reports have been published with prevalence rates from 0 to 100%. We aimed to find a detection algorithm of a biologically and thus clinically meaningful infection, applicable for high-throughput screening of frozen and formalin-fixed paraffin embedded (FFPE) specimens. By considering detection of HPV E6 oncogene expression in frozen biopsies as gold standard for a meaningful HPV infection, the value of several assays was evaluated on FFPE tumor specimens and sera of 48 HNSCC patients. The following assays were evaluated on FFPE tissue samples: HPV DNA general primer (GP)5+/6+ PCR, viral load analysis, HPV16 DNA FISH detection, HPV16 E6 mRNA RT-PCR, p16 immunostaining, and on corresponding serum samples detection of antibodies against the HPV16 proteins L1, E6 and E7. Comparing single assays on FFPE tissue samples detection of E6 expression by RT-PCR was superior, but application remains at present limited to HPV16 detection. Most suitable algorithm with 100% sensitivity and specificity appeared p16 immunostaining followed by GP5+/6+ PCR on the p16-positive cases. We show that clinically meaningful viral HPV infections can be more reliably measured in FFPE HNSCC samples in a standard and high throughput manner, paving the way for prognostic and experimental vaccination studies, regarding not only HNSCC, but possibly also cancer types with HPV involvement in subgroups such as penile and anal cancer

Molecular subtypes of pulmonary large-cell neuroendocrine carcinoma predict chemotherapy treatment outcome

Purpose:Previous genomic studies have identified two mutually exclusive molecular subtypes of large-cell neuroendocrine carcinoma (LCNEC): the RB1 mutated (mostly comutated with TP53) and the RB1 wild-type groups. We assessed whether these subtypes have a predictive value on chemotherapy outcome. Experimental Design: Clinical data and tumor specimens were retrospectively obtained from Netherlands Cancer Registry and Pathology Registry. Panel-consensus pathology revision confirmed the diagnosis of LCNEC in 148 of 232 cases. Next-generation sequencing (NGS) for TP53, RB1, STK11, and KEAP1 genes, as well as IHC for RB1 and P16 was performed on 79 and 109 cases, respectively, and correlated with overall survival (OS) and progression-free survival (PFS), stratifying for non-small cell lung cancer type chemotherapy including platinum + gemcitabine or taxanes (NSCLC-GEM/TAX) and platinum-etoposide (SCLC-PE). Results: RB1 mutation and protein loss were detected in 47% (n = 37) and 72% (n = 78) of the cases, respectively. Patients with RB1 wild-type LCNEC treated with NSCLC-GEM/TAX had a significantly longer OS [9.6; 95% confidence interval (CI), 7.7-11.6 months] than those treated with SCLC-PE [5.8 (5.5-6.1); P = 0.026]. Similar results were obtained for patients expressing RB1 in their tumors (P = 0.001). RB1 staining or P16 loss showed similar results. The same outcome for chemotherapy treatment was observed in LCNEC tumors harboring an RB1 mutation or lost RB1 protein. Conclusions: Patients with LCNEC tumors that carry a wild-type RB1 gene or express the RB1 protein do better with NSCLC-GEM/TAX treatment than with SCLC-PE chemotherapy. However, no difference was observed for RB1 mutated or with lost protein expression.</p

Multicenter Comparison of Molecular Tumor Boards in The Netherlands: Definition, Composition, Methods, and Targeted Therapy Recommendations

Background: Molecular tumor boards (MTBs) provide rational, genomics-driven, patient-tailored treatment recommendations. Worldwide, MTBs differ in terms of scope, composition, methods, and recommendations. This study aimed to assess differences in methods and agreement in treatment recommendations among MTBs from tertiary cancer referral centers in The Netherlands. Materials and Methods: MTBs from all tertiary cancer referral centers in The Netherlands were invited to participate. A survey assessing scope, value, logistics, composition, decision-making method, reporting, and registration of the MTBs was completed through on-site interviews with members from each MTB. Targeted therapy recommendations were compared using 10 anonymized cases. Participating MTBs were asked to provide a treatment recommendation in accordance with their own methods. Agreement was based on which molecular alteration(s) was considered actionable with the next line of targeted therapy. Results: Interviews with 24 members of eight MTBs revealed that all participating MTBs focused on rare or complex mutational cancer profiles, operated independently of cancer type–specific multidisciplinary teams, and consisted of at least (thoracic and/or medical) oncologists, pathologists, and clinical scientists in molecular pathology. Differences were the types of cancer discussed and the methods used to achieve a recommendation. Nevertheless, agreement among MTB recommendations, based on identified actionable molecular alteration(s), was high for the 10 evaluated cases (86%). Conclusion: MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational cancer profiles. We propose a “Dutch MTB model” for an optimal, collaborative, and nationally aligned MTB workflow. Implications for Practice: Interpretation of genomic analyses for optimal choice of target therapy for patients with cancer is becoming increasingly complex. A molecular tumor board (MTB) supports oncologists in rationalizing therapy options. However, there is no consensus on the most optimal setup for an MTB, which can affect the quality of recommendations. This study reveals that the eight MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational profiles. The Dutch MTB model is based on a collaborative and nationally aligned workflow with interinstitutional collaboration and data sharing