Virtual staining for digital pathology has great potential to enable spatial biology research, improve efficiency and reliability in the clinical workflow, as well as conserve tissue samples in a non-destructive manner. In this study, we demonstrate the feasibility of generating virtual stains for hematoxylin and eosin (H&E) and a multiplex immunofluorescence (mIF) immuno-oncology panel (DAPI, PanCK, PD-L1, CD3, CD8) from autofluorescence images of unstained non-small cell lung cancer tissue by combining high-throughput hyperspectral fluorescence microscopy and machine learning. Using domain-specific computational methods, we evaluated the accuracy of virtual H&E for histologic subtyping and virtual mIF for cell segmentation-based measurements, including clinically-relevant measurements such as tumor area, T cell density, and PD-L1 expression (tumor proportion score and combined positive score). The virtual stains reproduce key morphologic features and protein biomarker expressions at both tissue and cell levels compared to real stains, enable the identification of key immune phenotypes important for immuno-oncology, and show moderate to good performance across various evaluation metrics. This study extends our previous work on virtual staining from autofluorescence in liver disease and prostate cancer, further demonstrating the generalizability of this deep learning technique to a different disease (lung cancer) and stain modality (mIF).