The patient-specific landscape of the tumor microenvironment can be appreciated by using immunograms as integrated biomarkers, which may thus become a valuable resource for optimal personalized immunotherapy.
The importance of neoantigens for cancer immunity is now well‐acknowledged. However, there are diverse strategies for predicting and prioritizing candidate neoantigens, and thus reported neoantigen loads vary a great deal. To clarify this issue, we compared the numbers of neoantigen candidates predicted by four currently utilized strategies. Whole‐exome sequencing and RNA sequencing (RNA‐Seq) of four non‐small‐cell lung cancer patients was carried out. We identified 361 somatic missense mutations from which 224 candidate neoantigens were predicted using MHC class I binding affinity prediction software (strategy I). Of these, 207 exceeded the set threshold of gene expression (fragments per kilobase of transcript per million fragments mapped ≥1), resulting in 124 candidate neoantigens (strategy II). To verify mutant mRNA expression, sequencing of amplicons from tumor cDNA including each mutation was undertaken; 204 of the 207 mutations were successfully sequenced, yielding 121 mutant mRNA sequences, resulting in 75 candidate neoantigens (strategy III). Sequence information was extracted from RNA‐Seq to confirm the presence of mutated mRNA. Variant allele frequencies ≥0.04 in RNA‐Seq were found for 117 of the 207 mutations and regarded as expressed in the tumor, and finally, 72 candidate neoantigens were predicted (strategy IV). Without additional amplicon sequencing of cDNA, strategy IV was comparable to strategy III. We therefore propose strategy IV as a practical and appropriate strategy to predict candidate neoantigens fully utilizing currently available information. It is of note that different neoantigen loads were deduced from the same tumors depending on the strategies applied.
Use of an off-the-shelf pipeline is feasible but may not be satisfactory for most patients with non-small cell lung cancer. We recommend identifying personal mutations by comprehensive genome sequencing for developing neoantigen-targeted cancer immunotherapies.
Background Virtual-assisted lung mapping is a novel bronchoscopic preoperative lung marking technique in which virtual bronchoscopy is used to predict the locations of multiple dye markings. Post-mapping computed tomography is performed to confirm the locations of the actual markings. This study aimed to examine the accuracy of marking locations predicted by virtual bronchoscopy and elucidate the role of post-mapping computed tomography. Methods Automated and manual virtual bronchoscopy was used to predict marking locations. After bronchoscopic dye marking under local anesthesia, computed tomography was performed to confirm the actual marking locations before surgery. Discrepancies between marking locations predicted by the different methods and the actual markings were examined on computed tomography images. Forty-three markings in 11 patients were analyzed. Results The average difference between the predicted and actual marking locations was 30 mm. There was no significant difference between the latest version of the automated virtual bronchoscopy system (30.7 ± 17.2 mm) and manual virtual bronchoscopy (29.8 ± 19.1 mm). The difference was significantly greater in the upper vs. lower lobes (37.1 ± 20.1 vs. 23.0 ± 6.8 mm, for automated virtual bronchoscopy; p < 0.01). Despite this discrepancy, all targeted lesions were successfully resected using 3-dimensional image guidance based on post-mapping computed tomography reflecting the actual marking locations. Conclusions Markings predicted by virtual bronchoscopy were dislocated from the actual markings by an average of 3 cm. However, surgery was accurately performed using post-mapping computed tomography guidance, demonstrating the indispensable role of post-mapping computed tomography in virtual-assisted lung mapping.
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