We propose an approach to detection of essential genes/proteins required for cancer cell survival. A gene is considered essential if a mutation with high impact upon the function of encoded protein causes death of the cancer cell. We draw an analogy between essential cancer proteins and well-known Abraham Wald's work on estimating the plane critical areas using data on survivability of aircraft encountering enemy fire. Wald reasoned that parts with no bullet holes on the airplanes returned to the airbase from a combat flight are the most crucial ones for the airplane functioning: a hit in one of these parts downs an airplane, so it does not return back for the survey. We have envisaged that the airplane surface is a cancer genome and the bullets are somatic mutations with high impact upon protein function. Similarly we propose that genes specifically essential for tumor cell survival should carry less high-impact mutations in cancer cells compared to polymorphisms found in normal cells. We used data on mutations from the Cancer Genome Atlas and polymorphisms found in healthy humans (from 1000 Genomes Project) to predict 91 protein-coding genes essential for melanoma. These genes were selected according to several criteria, including negative selection, expression in melanocytes and decrease in the proportion of high-impact mutations in cancer compared with normal cells. The Gene Ontology analysis revealed enrichment of essential proteins related to membrane and cell periphery. We speculate that this could be a sign of immune system-driven negative selection of cancer neo-antigens. Another finding is the overrepresentation of semaphorin receptors, which can mediate distinctive signaling cascades and are involved in various aspects of tumor development. Cytokine receptors CCR5 and CXCR1 were also identified as cancer essential proteins and this is confirmed by other studies. Overall, our goal was to illustrate the idea of detecting proteins whose sequence integrity and functioning is important for cancer cell survival. Hopefully, this prediction of essential cancer proteins may point to new targets for anti-tumor therapies.