Masses of the ovary, fallopian tube, or surrounding tissues are common and found in up to 35% of premenopausal patients and 17% of postmenopausal patients. They are often diagnosed incidentally on physical examination or at the time of pelvic imaging. The differential diagnosis is broad and includes both benign and malignant lesions, as well as gynecologic and non-gynecologic lesions. The patient’s age, family history, physical exam, imaging findings, and serum marker levels can aid with narrowing the differential. Risk factors for ovarian cancer include age, family history of ovarian cancer or genetic predisposition for ovarian cancer, nulliparity, early menarche, late menopause, and endometriosis. Management aims include identifying need for emergent surgery, identifying malignancy, managing symptoms, and preserving fertility when appropriate. Management options for pelvic masses include expectant management, surveillance, and surgical management. While minimally invasive surgery is often the preferred surgical approach for removal of an adnexal mass, open surgery may be required for larger masses or those concerning for malignancy. Ovarian cancer is the second most common gynecologic malignancy after uterine carcinoma and is the deadliest gynecologic cancer in the United States. Approximately 95% of ovarian cancer arise from the epithelial cells and include high grade serous, low grade serous, endometrioid, clear cell, and mucinous carcinoma. Most patients have advanced staged disease at the time of ovarian cancer diagnosis. While surgery alone can cure most patients with early-stage disease, most patients present with advanced stage disease and will require a combination of both surgery and chemotherapy. The standard chemotherapy regimen includes a combination of platinum- and taxane-based chemotherapy. Five-year overall survival rates are 89% for stage I, 71% for stage II, 41% for stage III, and 20% for stage IV disease.

In the years following FDA approval of direct-to-consumer, genetic-health-risk/DTCGHR testing, millions of people in the US have sent their DNA to companies to receive personal genome health risk information without physician or other learned medical professional involvement. In Personal Genome Medicine, Michael J. Malinowski examines the ethical, legal, and social implications of this development. Drawing from the past and present of medicine in the US, Malinowski applies law, policy, public and private sector practices, and governing norms to analyze the commercial personal genome sequencing and testing sectors and to assess their impact on the future of US medicine. Written in relatable and accessible language, the book also proposes regulatory reforms for government and medical professionals that will enable technological advancements while maintaining personal and public health standards.

To understand what genes “do,” we have to consider what happens during development. The first and most striking evidence that the local environment matters for the outcome of development was provided by the experiments of embryologists Wilhelm Roux and Hans Driesch in the late nineteenth and early twentieth centuries. Roux had hypothesized that during the cell divisions of the embryo, hereditary particles were unevenly distributed in its cells, thus driving their differentiation. This view entailed that even the first blastomeres (the cells emerging from the first few divisions of the zygote – that is, the fertilized ovum) would each have different hereditary material and that the embryo would thus become a kind of mosaic. Roux decided to test this hypothesis. He assumed that if it were true, destroying a blastomere in the two-cell or the four-cell stage would produce a partially deformed embryo. If it were not true, then the destruction of a blastomere would have no effect. With a hot sterilized needle, Roux punctured one of the blastomeres in a two-cell frog embryo that was thus killed. The other blastomere was left to develop. The outcome was a half-developed embryo; the part occupied by the punctured blastomere was highly disorganized and undifferentiated, whereas those cells resulting from the other blastomere were well-developed and partially differentiated. This result stood as confirmation for Roux’s hypothesis.

During the 1970s, more puzzling observations were made. The first was that the genome of animals contained large amounts of DNA with unique sequences that should correspond to a larger number of genes than anticipated. It was also observed that the RNA molecules in the nuclei of cells were much longer than those found outside the nucleus, in the cytoplasm. These observations started making sense in 1977, when sequences of mRNA were compared to the corresponding DNA sequences. It was shown that certain sequences that existed in the DNA did not exist in the mRNA, and that therefore they must have been somehow removed. It was thus concluded that the genes encoding various proteins in eukaryotes included both coding sequences and ones that were not included in the mRNA that would reach the ribosomes for translation. These “removed” sequences were called introns, to contrast them with the ones that were expressed in translation, which were called exons. The procedure that removed the intron sequences from the initial mRNA and that left only the exon sequences in the mature mRNA was named “RNA splicing.”

One important, and for some the most surprising, conclusion of genome-wide association studies (GWAS) has been that in most cases numerous single nucleotide polymorphism (SNPs) in several genes were found to be associated with the development of a characteristic or the risk of developing a disease. As already mentioned, the main conclusion has been that the relationship between genes and characteristics or diseases is usually a many-to-many one, as many genes may be implicated in the same condition, and the same gene may be implicated in several different conditions. In fact, the same allele may be protective for one disease but increase the risk for another. For example, a variation in the PTPN22 (protein tyrosine phosphatase, nonreceptor type 22) gene on chromosome 1 seems to protect against Crohn’s disease but to predispose to autoimmune diseases. In other cases, certain variants are associated with more than one disease, such as the JAZF1 (JAZF1 zinc finger 1) gene on chromosome 7 that is implicated in prostate cancer and in type 2 diabetes. Therefore, we should forget the simple scheme of gene 1 → condition 1/gene 2 → condition 2, and adopt a richer – and certainly more complicated – representation of the relationship between genes and disease. Additional GWAS on more variants in larger populations might provide a better picture in the future. But insofar as we do not understand all biological processes in detail, all we are left with are probabilistic associations between genes and characteristics (or diseases). The “associated gene” may be informative, but its explanatory potential and clinical value are limited – at least for now.

This chapter is about the public image of genes. But what exactly do we mean by “public”? Here, I use the word as a noun or an adjective vaguely, in order to refer to all ordinary people who are not experts in genetics. I thus contrast them with scientists who are experts in genetics – that is, who have mastered genetics-related knowledge and skills, who practice these as their main occupation, and who have valid genetics-related credentials, confirmed experience, and affirmation by their peers. I must note that both “experts” and “the public” are complex categories that depend on the context and that change over time. There is no single group of nonexperts that we can define as “the” public, as people around the world differ in their perceptions of science, depending on their cultural contexts. We had therefore better refer to “publics.” The differences among experts nowadays might be less significant than those among nonexperts, given today’s global scientific communities, but they do exist. Finally, both the categories of experts and publics have changed across time, depending, on the one hand, on the level of experts’ knowledge and understanding of the natural world, and, on the other hand, on publics’ attitudes toward that knowledge and understanding.

If you were taught Mendelian genetics at school (see Figures 2.1 and 2.2) you should be aware that it is an oversimplified model that does not work for most cases of inherited characteristics. Human eye color is a textbook example of a monogenic characteristic. It refers to the color of the iris – the colored circle in the middle of the eye. The iris comprises two tissue layers, an inner one called the iris pigment epithelium and an outer one called the anterior iridial stroma. It is the density and cellular composition of the latter that mostly affects the color of the iris. The melanocyte cells of the anterior iridial stroma store melanin in organelles called melanosomes. White light entering the iris can absorb or reflect a spectrum of wavelengths, giving rise to the three common iris colors (blue, green–hazel, and brown) and their variations. Blue eyes contain minimal pigment levels and melanosome numbers; green–hazel eyes have moderate pigment levels and melanosome numbers; and brown eyes are the result of high melanin levels and melanosome numbers. Textbook accounts often explain that a dominant allele B is responsible for brown color, whereas a recessive allele b is responsible for blue color (Figure 4.1). According to such accounts, parents with brown eyes can have children with blue eyes, but it is not possible for parents with blue eyes to have children with brown eyes. This pattern of inheritance was first described at the beginning of the twentieth century and it is still taught in schools, although it became almost immediately evident that there were exceptions, such as that two parents with blue eyes could have offspring with brown or dark hazel eyes.

Perhaps you were taught at school that genetics began with Gregor Mendel. Because of his experiments with peas, Mendel is considered to be a pioneer of genetics and the person who discovered the laws of heredity. According to the model of “Mendelian inheritance,” things are rather simple and straightforward with inherited characteristics. Some alleles are dominant – that is, they impose their effects on other alleles that are recessive. An individual who carries two recessive alleles exhibits the respective “recessive” characteristic, whereas a single dominant allele is sufficient for the “dominant” version of the characteristic to appear. In this sense, particular genes determine particular characteristics (e.g., seed color in peas), and particular alleles of those genes determine particular versions of the respective characteristics. Mendel, the story goes, discovered that characteristics are controlled by hereditary factors, the inheritance of which follows two laws: the law of segregation and the law of independent assortment.

Direct-to-consumer (DTC) genetic testing has raised questions about the accuracy of the test results, which may raise potential novel liability issues. This chapter examines potential liability exposures relating to DTC genetic testing in two main contexts. The first is potential liability for the DTC provider itself for erroneous results. Key issues in such cases will be the validity and effect of the representations and disclosures that the DTC company provides, which often informs the consumer that the results should not be relied on for medical care, disclaim any liability for any harms that may result from such reliance, and require any disputes to be resolved by mandatary arbitration. The second potential type of case is against health care providers who are presented with DTC results by their patients. Providers will often be put in a “damned if they do, damned if they don’t” predicament by such results, as both ignoring or relying on the DTC test results could create potential liabilities in certain situations. While providers can usually protect themselves by requiring validation of the DTC results by an independent laboratory, time and economic constraints may make this infeasible in some cases.