Several diseases, such as cancers, are caused by the presence of deleterious alleles that affect the function of a gene. In the case of cancer, most of the mutations are somatic cell mutations—mutations that have occurred after fertilization. These mutations will not be passed on to future generations. However, there are some variants that are present in the germline and these will be inherited. A small percentage of these variants will cause cancer directly but most will just indicate a predisposition to develop cancer.
There are a host of other diseases that have a genetic component and the responsible alleles can also be present in the germline or due to somatic cell mutations.
Over the past fifty years or so there has been a lot of hype associated with the latest technological advances and the ability to detect deleterious germline mutations. The general public has been repeatedly told that we will soon be able to identify all disease-causing alleles and this will definitely lead to incredible medical advances in treating these diseases. Just yesterday, for example, I posted an article on predictions made by The National Genome Research Institute (USA) who predicts that by 2030,
The clinical relevance of all encountered genomic variants will be readily predictable, rendering the diagnostic designation ‘variant of uncertain significance (VUS)’ obsolete.
Similar predictions, in various forms, were made when the human genome project got under way and at various time afterword. First there was the 1000 genomes project then there was the 100,000 genome project and, of course, ENCODE. The problem is that genomics hasn't lived up to these expectations and there's a very good reason for that: it's because the problem is a lot more difficult than it seems.
One of the Facebook groups that I follow (Modern Genetics & Technology)1 alerted me to a recent paper in JAMA that addressed the problem of genomics accuracy and the prediction of pathogenic variants. I'm posting the complete abstract so you can see the extent of the problem.
AlDubayan, S.H., Conway, J.R., Camp, S.Y., Witkowski, L., Kofman, E., Reardon, B., Han, S., Moore, N., Elmarakeby, H. and Salari, K. (2020) Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma. JAMA 324:1957-1969. [doi: 10.1001/jama.2020.20457]
Importance Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection.
Objective To evaluate if deep learning approaches identify more germline pathogenic variants in patients with cancer.
Design Setting, and Participants A cross-sectional study of a standard germline detection method and a deep learning method in 2 convenience cohorts with prostate cancer and melanoma enrolled in the US and Europe between 2010 and 2017. The final date of clinical data collection was December 2017.
Exposures Germline variant detection using standard or deep learning methods.
Main Outcomes and Measures The primary outcomes included pathogenic variant detection performance in 118 cancer-predisposition genes estimated as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The secondary outcomes were pathogenic variant detection performance in 59 genes deemed actionable by the American College of Medical Genetics and Genomics (ACMG) and 5197 clinically relevant mendelian genes. True sensitivity and true specificity could not be calculated due to lack of a criterion reference standard, but were estimated as the proportion of true-positive variants and true-negative variants, respectively, identified by each method in a reference variant set that consisted of all variants judged to be valid from either approach.
Results The prostate cancer cohort included 1072 men (mean [SD] age at diagnosis, 63.7 [7.9] years; 857 [79.9%] with European ancestry) and the melanoma cohort included 1295 patients (mean [SD] age at diagnosis, 59.8 [15.6] years; 488 [37.7%] women; 1060 [81.9%] with European ancestry). The deep learning method identified more patients with pathogenic variants in cancer-predisposition genes than the standard method (prostate cancer: 198 vs 182; melanoma: 93 vs 74); sensitivity (prostate cancer: 94.7% vs 87.1% [difference, 7.6%; 95% CI, 2.2% to 13.1%]; melanoma: 74.4% vs 59.2% [difference, 15.2%; 95% CI, 3.7% to 26.7%]), specificity (prostate cancer: 64.0% vs 36.0% [difference, 28.0%; 95% CI, 1.4% to 54.6%]; melanoma: 63.4% vs 36.6% [difference, 26.8%; 95% CI, 17.6% to 35.9%]), PPV (prostate cancer: 95.7% vs 91.9% [difference, 3.8%; 95% CI, –1.0% to 8.4%]; melanoma: 54.4% vs 35.4% [difference, 19.0%; 95% CI, 9.1% to 28.9%]), and NPV (prostate cancer: 59.3% vs 25.0% [difference, 34.3%; 95% CI, 10.9% to 57.6%]; melanoma: 80.8% vs 60.5% [difference, 20.3%; 95% CI, 10.0% to 30.7%]). For the ACMG genes, the sensitivity of the 2 methods was not significantly different in the prostate cancer cohort (94.9% vs 90.6% [difference, 4.3%; 95% CI, –2.3% to 10.9%]), but the deep learning method had a higher sensitivity in the melanoma cohort (71.6% vs 53.7% [difference, 17.9%; 95% CI, 1.82% to 34.0%]). The deep learning method had higher sensitivity in the mendelian genes (prostate cancer: 99.7% vs 95.1% [difference, 4.6%; 95% CI, 3.0% to 6.3%]; melanoma: 91.7% vs 86.2% [difference, 5.5%; 95% CI, 2.2% to 8.8%]).
Conclusions and Relevance Among a convenience sample of 2 independent cohorts of patients with prostate cancer and melanoma, germline genetic testing using deep learning, compared with the current standard genetic testing method, was associated with higher sensitivity and specificity for detection of pathogenic variants. Further research is needed to understand the relevance of these findings with regard to clinical outcomes.
It's really difficult to understand this paper since there are many terms that I'd have to research more thoroughly; for example, does "germline whole-exon sequencing" mean that only sperm or egg DNA was sequenced and that every single exon in the entire genome was sequenced? Were exons in noncoding genes also sequenced?
I found it much more useful to look at the accompanying editorial by Gregory Feero.
Feero, W.G. (2020) Bioinformatics, Sequencing Accuracy, and the Credibility of Clinical Genomics. JAMA 324:1945-1947. [doi: 10.1001/jama.2020.19939]
Ferro explains that the main problem is distinguishing real pathogenic variants from false positives and this can only be accomplished by first sequencing and assembling the DNA and then using various algorithms to focus on important variants. Then there's the third step.
The third step, which often requires a high level of clinical expertise, sifts through detected potentially deleterious variations to determine if any are relevant to the indication for testing. For example, exome sequencing ordered for a patient with unexplained cardiomyopathy might harbor deleterious variants in the BRCA1 gene which, while a potentially important incidental finding, does not provide a plausible molecular diagnosis for the cardiomyopathy. The complexity of the bioinformatics tools used in these 3 steps is considerable.
It's that third step that's analyzed in the AlDubayan et al. paper and one of the tools used is a deep-learning (AI) algorithm. However, the training of this algorithm requiries considerable clinical expertise and testing it requires a gold standard set of variants to serve as an internal control. As you might have guessed, that gold standard doesn't exist because the whole point of the genomics is to identify perviously unknown deleterious alleles.
Ferro warns us that "clinical genome sequencing remains largely unregulated and accuracy is highly dependant on the expertise of individual testing laboratories." He concludes that genomics still has a long way to go.
The genomics community needs to act as a coherent body to ensure reproducibility of outcomes from clinical genome or exome sequencing, or provide transparent quality metrics for individual clinical laboratories. Issues related to achieving accuracy are not new, are not limited to bioinformatics tools, and will not be surmounted easily. However, until analytic and clinical validity are ensured, conversations about the potential value that genome sequencing brings to clinical situations will be challenging for clinical centers, laboratories that provide sequencing services, and consumers. For the foreseeable future, nongeneticist clinicians should be familiar with the quality of their chosen genome-sequencing laboratory and engage expert advice before changing patient management based on a test result.
I'm guessing that Gregory Feero doesn't think that in nine years (2030) "The clinical relevance of all encountered genomic variants will be readily predictable."
1. I do NOT recommend this group. It's full of amateurs who resist leaning and one of it's main purposes is to post copies of pirated textbooks in its files. The group members get very angry when you tell them that what they are doing is illegal!
