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.

Endometrial cancer is the most common gynaecologic malignancy in high-income countries, with a rising incidence largely driven by reproductive factors, obesity, and prolonged exposure to unopposed oestrogens. Although most cases are sporadic, approximately 2–5% are associated with hereditary cancer syndromes, of which Lynch syndrome represents the most important contributor. Lynch syndrome results from germline mutations in DNA mismatch repair (MMR) genes and is associated with a substantially increased lifetime risk of endometrial cancer, reaching up to 71% in carriers of MutS homologue 6 (MSH6) mutations. Hereditary cancer predisposition typically follows an autosomal dominant inheritance pattern and may be suspected based on clinical warning signs such as early disease onset, multiple primary malignancies, a strong family history, or the presence of microsatellite instability in tumour tissue. In addition to Lynch syndrome, rarer genetic conditions—including Cowden syndrome (PTEN), Li–Fraumeni syndrome (TP53), polymerase proofreading–associated polyposis (POLE/POLD1), and hereditary breast and ovarian cancer syndromes (BRCA1/2)—also contribute to hereditary endometrial cancer risk. Recognition of these genetic backgrounds is essential for accurate diagnosis, personalised surveillance, and the implementation of targeted preventive and therapeutic strategies. Despite major advances in molecular diagnostics, hereditary endometrial cancer remains frequently underdiagnosed, leading to missed opportunities for cancer prevention among affected individuals and their families. This comprehensive review summarises current evidence on hereditary predispositions to endometrial cancer, with a particular emphasis on Lynch syndrome, and discusses underlying genetic mechanisms, inheritance patterns, diagnostic strategies, and clinical implications for screening, genetic counselling, and treatment optimisation.

Cancer precision medicine is moving beyond static, single-analyte profiling toward a dynamic view of tumour biology and treatment response. A multiomics approach, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic information, offers a more comprehensive framework for understanding tumour heterogeneity and therapeutic vulnerability. This review examines the emerging landscape of clinically available, translational, and experimental approaches, spanning commercially available platforms such as CureResponse and Exacta, single-cell and spatial technologies, AI-assisted plasma multiomics, and functional platforms including BH3 profiling and patient-derived organoid drug screening. We also discuss longitudinal liquid biopsy approaches for minimal residual disease assessment and recurrence monitoring. Together, these technologies may extend conventional biopsy by enabling increasingly multidimensional, spatially resolved, and longitudinal assessment of cancer. However, their clinical implementation remains limited by challenges in analytical standardisation, data integration, biological interpretation, validation, and demonstrated clinical utility. The convergence of multiomics, single-cell and spatial profiling, artificial intelligence, and functional testing is therefore reshaping precision oncology, from describing what a tumour is to predicting how it may behave and respond to treatment.