Citations

Abstract Aims Spectrum bias can arise when a diagnostic test is derived from study populations with different disease spectra than the target population, resulting in poor generalizability. We used a real-world artificial intelligence (AI)-derived algorithm to detect severe aortic stenosis (AS) to experimentally assess the effect of spectrum bias on test performance. Methods and results All adult patients at the Mayo Clinic between 1 January 1989 and 30 September 2019 with transthoracic echocardiograms within 180 days after electrocardiogram (ECG) were identified. Two models were developed from two distinct patient cohorts: a whole-spectrum cohort comparing severe AS to any non-severe AS and an extreme-spectrum cohort comparing severe AS to no AS at all. Model performance was assessed. Overall, 258 607 patients had valid ECG and echocardiograms pairs. The area under the receiver operator curve was 0.87 and 0.91 for the whole-spectrum and extreme-spectrum models, respectively. Sensitivity and specificity for the whole-spectrum model was 80% and 81%, respectively, while for the extreme-spectrum model it was 84% and 84%, respectively. When applying the AI-ECG derived from the extreme-spectrum cohort to patients in the whole-spectrum cohort, the sensitivity, specificity, and area under the curve dropped to 83%, 73%, and 0.86, respectively. Conclusion While the algorithm performed robustly in identifying severe AS, this study shows that limiting datasets to clearly positive or negative labels leads to overestimation of test performance when testing an AI algorithm in the setting of classifying severe AS using ECG data. While the effect of the bias may be modest in this example, clinicians should be aware of the existence of such a bias in AI-derived algorithms.

Abstract Objectives To demonstrate clinicopathologic features and evaluate the clonality of double PIK3CA alterations in colorectal cancers (CRCs). Methods Clonality was examined in 13 CRCs with double PIK3CA alterations (1.7% of CRCs or 9.6% of PIK3CA-mutated CRCs). Multiregional analyses were performed to confirm subclonal PIK3CA alterations. Results PIK3CA alterations were detected within exon 9 (51%), exon 20 (23%), exon 1 (15%), and exon 7 (6.0%). CRCs with exon 7 alterations showed a significantly higher incidence of double PIK3CA alterations. Most double PIK3CA alterations consisted of a hotpsot alteration and an uncommon alteration; they were often clonal and present within a single tumor population. Multiregional analyses of CRCs with predicted subclonal double-alterations revealed multiclonal CRCs with divergent PIK3CA variant status originating from a common APC- and KRAS-mutated founder lineage of adenoma. Conclusions The findings supported multiclonal CRCs resulting from parallel evolution during the progression from adenoma to adenocarcinoma within the mitogen-activated protein kinase pathway, as previously demonstrated, or the mammalian target of rapamycin pathway. Further studies are warranted to elucidate clinical significance and potential targeted therapy for CRC patients with double PIK3CA alterations and impacts on clinical decision-making in patients with multiclonal CRCs harboring divergent PIK3CA mutational status.

Endosomal sorting complexes required for transport (ESCRT-0, -I, -II, -III) execute cargo sorting and intralumenal vesicle (ILV) formation during conversion of endosomes to multivesicular bodies (MVBs). The AAA-ATPase Vps4 regulates the ESCRT-III polymer to facilitate membrane remodeling and ILV scission during MVB biogenesis. Here, we show that the conserved V domain of ESCRT-associated protein Bro1 (the yeast homologue of mammalian proteins ALIX and HD-PTP) directly stimulates Vps4. This activity is required for MVB cargo sorting. Furthermore, the Bro1 V domain alone supports Vps4/ESCRT–driven ILV formation in vivo without efficient MVB cargo sorting. These results reveal a novel activity of the V domains of Bro1 homologues in licensing ESCRT-III–dependent ILV formation and suggest a role in coordinating cargo sorting with membrane remodeling during MVB sorting. Moreover, ubiquitin binding enhances V domain stimulation of Vps4 to promote ILV formation via the Bro1–Vps4–ESCRT-III axis, uncovering a novel role for ubiquitin during MVB biogenesis in addition to facilitating cargo recognition.

In this study we aim to examine gene–environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (p-2df = 1.2 × 10−3). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (p-2df = 1.1 × 10−4). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk.