Tumors harbor multiple genetic alterations, yet treatment decisions are commonly based on single biomarkers, leading to underutilization of genomic information by comprehensive molecular tests, uncertainty in clinical practice, and frequent treatment failures. Although molecular tumor boards can assist personalized treatments, this process is not scalable or standardized, resulting in highly discordant recommendations. Validated digital solutions for personalized decision support are highly needed. The Digital Drug Assignment (DDA) system is a computational reasoning model that scores treatment options based on the full tumor genomic data. We retrospectively analyzed data of 111 lung cancer patients and found that high-score MTAs (1000≦DDA score) provided significant clinical benefit over other treatments, in terms of ORR, PFS, and OS. These results demonstrate that the DDA system is predictive of relative benefit of the various agents used in lung cancer care. Digital drug assignment can potentially address challenges with complex molecular profiles in routine clinical settings.

The clinical manifestations of coronavirus disease (COVID-19) are highly variable, ranging from asymptomatic cases to life-threatening complications and death. Severe disease progression is more common in older individuals and males, as well as in the presence of various comorbidities. Beyond these risk factors, the intrinsic characteristics of the virus and the host genetic factors also contribute to the heterogeneous clinical course of COVID-19. Genetic research is fundamental in understanding the biological mechanisms underlying congenital diseases, identifying the genes and proteins responsible for the susceptibility to various inherited conditions, recognizing therapeutically relevant targets, suggesting drug repurposing, and clarifying causal relationships for modifiable environmental risk factors. Although these studies typically take a long time to conduct, especially to translate their findings into clinical practice, the scientific community has swiftly uncovered genetic signals underlying the diverse COVID-19 phenotypes. In this review, in addition to a concise summary of SARS-CoV-2 recognition and the initial steps of the immune responses, we aim to provide an overview of the literature concerning the genetic factors associated with susceptibility to the disease and its severe progression. We also review the pioneering research in identifying the affected genes and the most significant genome-wide association studies, covering both common and rare genetic variants, which have greatly contributed to understand the etiology of the disease and have guided effective COVID-19 treatment during the most challenging times.