A recent study found that a blood test could help diagnose ovarian cancer faster and more accurately, particularly for women under the age of 50 (Cancers 2022; doi: 10.3390/cancers14092124).
Many women’s nonspecific symptoms make early-stage ovarian cancer diagnosis difficult. Guidelines in many countries currently recommend CA-125 as a first-line test for ovarian cancer in women presenting with possible symptoms of the disease, followed by an ultrasound after abnormal CA-125 results. Specialist referral occurs after abnormal ultrasound.
Some women have false negative CA-125, a finding that delays diagnosis and affects survival. Meanwhile, human epididymis protein 4 (HE4), a relatively new blood biomarker for ovarian cancer, has shown promise in the hospital setting but has not been evaluated in primary care.
The researchers studied whether HE4 would improve ovarian cancer diagnosis in women with symptoms in a primary care setting. They investigated the diagnostic accuracy of HE4 alone and in combination with CA-125 for ovarian cancer in symptomatic women attending primary care by testing general practitioner-requested CA-125 samples in a large teaching hospital in Manchester, England. They tracked cancer outcomes for 12 months. Due to a low incidence of ovarian cancer in primary, the cohort was enriched with presurgical samples from 81 ovarian cancer patients. The researchers calculated Risk of Ovarian Malignancy Algorithm (ROMA) using an age of 51 as a surrogate for menopause and determined conventional diagnostic accuracy metrics.
Out of a total of 1,229 patients, 82 had ovarian cancer. Overall, ROMA performed best, with an area under the curve (AUC) of 0.96. ROMA performed particularly well in women under 50 years old. Among these patients, the combination of CA-125 and HE4 (with either marker positive) had a sensitivity of 100% and specificity of 80.1%. In women over 50, ROMA had a sensitivity of 84.4% and specificity of 87.2%.
The researchers call for validation of their results in a much larger sample.
Rapid Genetic Point-of-Care Test Could Avoid Aminoglycoside Toxicity in Newborns
Rapid point-of-care (POC) testing for a rare genetic variant could avoid toxicity in newborns treated with aminoglycosides for gram-negative infections, according to a recent study (JAMA Pediatr 2022; doi: 10.1001/jamapediatrics.2022.0187).
Newborns who get aminoglycosides and have the m.1555A>G variant in MT-RNR1 can suffer profound and irreversible hearing loss. But timeliness in treating these infections is also critical, with experts recommending antibiotics be delivered within an hour of any decision to treat sepsis. While current genotyping approaches can alert clinicians of the need for alternative antibiotics, they take several days and are not useful in acute settings.
The researchers developed a rapid genotyping platform for the m.1555A>G variant and assessed whether the hospital could implement the technology successfully to avoid aminoglycoside-induced ototoxicity (AIO)—and without disrupting normal clinical practice in neonatal intensive care units. In a prospective implementation trial, they aimed to assess the proportion of neonates successfully tested for the variant among all infants prescribed antibiotics, whether implementation was negatively associated with routine clinical practice, and performance of the system.
Among 751 newborns with a median age of 2.5 days, the test produced genotyping results for the m.1555s>G variant in 26 minutes. Preclinical validation demonstrated 100% sensitivity and specificity. Testing identified three newborns with the variant, all of whom avoided aminoglycosides. Overall, 80.6% of infants administered antibiotics were tested for the variant without disrupting normal clinical practice.
Based on the population frequency of the m.1555A>G variant, and worldwide use of aminoglycosides in more than 7 million neonates each year, the adoption of this POC test could potentially prevent thousands of AIO cases annually. There are other acute clinical scenarios where knowledge of an individual’s genotype could be used to improve outcomes as well, the authors noted.
The researchers also pointed out that the SARS-CoV-2 pandemic has led to the proliferation of in vitro diagnostic systems that also could be redeployed for rapid genotyping.
Complementary Cannabinoid Screening Method Proposed
A universal screening assay and machine learning combination approach may complement conventional analytical methods for detecting synthetic cannabinoid receptor agonists (SCRAs), according to recent research (Clin Chem 2022; doi: 10.1093/clinchem/hvac027).
SCRAs are often much more active at the CB1 cannabinoid receptor than D9-THC, the prime psychoactive compound of the traditional recreational drug cannabis. Healthcare providers need information about SCRA user preferences, more toxic analogues, and rapidly proliferating new SCRAs because high CB1 cannabinoid receptor activity is associated with serious adverse health effects and emergency department visits.
Yet current SCRA screening strategies—like chromatography coupled to high-resolution mass spectrometry—are time consuming, expensive, and likely to miss low subnanogram per milliliter SCRA concentrations in body fluids. Meanwhile, activity-based bioassays have shown promise as a universal first-line screening tool for SCRAs that complement conventional targeted and untargeted analytical methods.
The researchers assessed an activity-based method for detecting newly circulating SCRAs, compared it with liquid chromatography coupled to high-resolution mass spectrometry, and evaluated their own machine learning models to reduce the screening workload by automating interpretation of the activity-based screening output. They tested their approach on 968 samples from adult emergency patients with acute recreational drug or NPS toxicity at a London hospital.
Of the 149 samples with analytically confirmed SCRAs, the approach had a sensitivity of 94.6% and a specificity of 98.5%. Findings also demonstrated rapid changes in the illicit drug market. The researchers detected six different SCRAs or their metabolites, only two of which they found in a similar 2019 study.
The model includes tradeoffs between having experts manually annotate samples or having to test more samples afterwards for confirmation. Expert review remains necessary to maintain high sensitivity and specificity of manual scoring. However, the machine learning approach could potentially speed up sample scoring and reduce workload, making it a good first-line screening approach to complement conventional analytic methods, the researchers noted.