Abstract
Lung cancer with the highest number of new cases diagnosed in Europe and in Poland, remains an example of malignancy with a very poor prognosis despite the recent progress in medicine. Different treatment strategies are now available for cancer therapy based on its type, molecular subtype and other factors including overall health, the stage of disease and cancer molecular profile. Immunotherapy is emerging as a potential addition to surgery, chemotherapy, radiotherapy or other targeted therapies, but also considered a mainstay therapy mode. This combination is an area of active investigation in order to enhance efficacy and overcome resistance. Due to the complexity and dynamic of cancer's ecosystem, novel therapeutic targets and strategies need continued research into the cellular and molecular mechanisms within the tumour microenvironment. From the genetic point of view, several signatures ranging from a few mutated genes to hundreds of them have been identified and associated with therapy resistance and metastatic potential. ML techniques and AI can enhance the predictive potential of genetic signatures and model the prognosis. Here, we present the overview of already existing treatment approaches, the current findings of key aspects of immunotherapy, such as immune checkpoint inhibitors (ICIs), existing molecular biomarkers like PD-L1 expression, tumour mutation burden, immunoscore, and neoantigens, as well as their roles as predictive markers for treatment response and resistance.