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Severe solar flare events can result in radio and electrical grid blackouts and damage to satellites. To protect critical infrastructure on Earth and in orbit, solar flare events must be predicted reliably. Deep learning models have been effective for solar flare prediction, but the black-box nature of deep learning makes extracting meaningful physics from such models difficult. We propose a solar flare prediction approach based on time-series shapelets, exploiting the matrix profile for efficient shapelet mining. Our aim is to demonstrate that flare prediction models based on time-series shapelets can make accurate predictions and provide meaningful physical insights into solar flare genesis. We mine time-series shapelets from each of the 24 magnetic field parameters given in the SWAN-SF dataset. We then evaluate the quality of the mined shapelets, and leverage the shapelets to create an interpretable solar flare prediction model. We demonstrate that this model is capable of predicting flare events with a true skill statistic comparable to state-of-the-art models.

Background The clonoSEQ® Assay (Adaptive Biotechnologies Corporation, Seattle, USA) identifies and tracks unique disease-associated immunoglobulin (Ig) sequences by next-generation sequencing of IgH, IgK, and IgL rearrangements and IgH-BCL1/2 translocations in malignant B cells. Here, we describe studies to validate the analytical performance of the assay using patient samples and cell lines. Methods Sensitivity and specificity were established by defining the limit of detection (LoD), limit of quantitation (LoQ) and limit of blank (LoB) in genomic DNA (gDNA) from 66 patients with multiple myeloma (MM), acute lymphoblastic leukemia (ALL), or chronic lymphocytic leukemia (CLL), and three cell lines. Healthy donor gDNA was used as a diluent to contrive samples with specific DNA masses and malignant-cell frequencies. Precision was validated using a range of samples contrived from patient gDNA, healthy donor gDNA, and 9 cell lines to generate measurable residual disease (MRD) frequencies spanning clinically relevant thresholds. Linearity was determined using samples contrived from cell line gDNA spiked into healthy gDNA to generate 11 MRD frequencies for each DNA input, then confirmed using clinical samples. Quantitation accuracy was assessed by (1) comparing clonoSEQ and multiparametric flow cytometry (mpFC) measurements of ALL and MM cell lines diluted in healthy mononuclear cells, and (2) analyzing precision study data for bias between clonoSEQ MRD results in diluted gDNA and those expected from mpFC based on original, undiluted samples. Repeatability of nucleotide base calls was assessed via the assay’s ability to recover malignant clonotype sequences across several replicates, process features, and MRD levels. Results LoD and LoQ were estimated at 1.903 cells and 2.390 malignant cells, respectively. LoB was zero in healthy donor gDNA. Precision ranged from 18% CV (coefficient of variation) at higher DNA inputs to 68% CV near the LoD. Variance component analysis showed MRD results were robust, with expected laboratory process variations contributing ≤3% CV. Linearity and accuracy were demonstrated for each disease across orders of magnitude of clonal frequencies. Nucleotide sequence error rates were extremely low. Conclusions These studies validate the analytical performance of the clonoSEQ Assay and demonstrate its potential as a highly sensitive diagnostic tool for selected lymphoid malignancies.

A polymerase-chain-reaction assay of 21 genes performed on paraffin-embedded samples from women with node-negative, estrogen-receptor–positive breast cancer was the basis for calculating a score for the risk of distant recurrence. The difference in risk between women with low and high recurrence scores was significant. The recurrence score also predicted overall survival. An assay of 21 genes was the basis for calculating the risk of distant recurrence. The difference in risk between women with low and high recurrence scores was significant. Over the past two decades, the molecular dissection of cancer has increased our understanding of the pathways that are altered in neoplastic cells. 1 , 2 Nevertheless, the diagnosis of cancer and decisions about its treatment still rely largely on classic histopathological and immunohistochemical techniques. A more quantitative approach to diagnosis and rational individualization of treatment are needed. Large clinical trials, such as National Surgical Adjuvant Breast and Bowel Project (NSABP) trials B-14 and B-20, have demonstrated the benefit of tamoxifen and chemotherapy in women who have node-negative, estrogen-receptor–positive breast cancer. 3 – 5 However, since the likelihood of distant recurrence in patients treated . . .

Background The Onco type DX ® Colon Cancer Assay is a new diagnostic test for determining the likelihood of recurrence in stage II colon cancer patients after surgical resection using fixed paraffin embedded (FPE) primary colon tumor tissue. Like the Onco type DX Breast Cancer Assay, this is a high complexity, multi-analyte, reverse transcription (RT) polymerase chain reaction (PCR) assay that measures the expression levels of specific cancer-related genes. By capturing the biology underlying each patient's tumor, the Onco type DX Colon Cancer Assay provides a Recurrence Score (RS) that reflects an individualized risk of disease recurrence. Here we describe its analytical performance using pre-determined performance criteria, which is a critical component of molecular diagnostic test validation. Results All analytical measurements met pre-specified performance criteria. PCR amplification efficiency for all 12 assays was high, ranging from 96% to 107%, while linearity was demonstrated over an 11 log 2 concentration range for all assays. Based on estimated components of variance for FPE RNA pools, analytical reproducibility and precision demonstrated low SDs for individual genes (0.16 to 0.32 C T s), gene groups (≤0.05 normalized/aggregate C T s) and RS (≤1.38 RS units). Conclusions Analytical performance characteristics shown here for both individual genes and gene groups in the Onco type DX Colon Cancer Assay demonstrate consistent translation of specific biology of individual tumors into clinically useful diagnostic information. The results of these studies illustrate how the analytical capability of the Onco type DX Colon Cancer Assay has enabled clinical validation of a test to determine individualized recurrence risk after colon cancer surgery.

The current standard of care measures for kidney function, proteinuria, and serum creatinine (SCr) are poor predictors of early-stage kidney disease. Measures that can detect chronic kidney disease in its earlier stages are needed to enable therapeutic intervention and reduce adverse outcomes of chronic kidney disease. We have developed the Kidney Injury Test (KIT) and a novel KIT Score based on the composite measurement and validation of multiple biomarkers across a unique set of 397 urine samples. The test is performed on urine samples that require no processing at the site of collection and without target sequencing or amplification. We sought to verify that the pre-defined KIT test, KIT Score, and clinical thresholds correlate with established chronic kidney disease (CKD) and may provide predictive information on early kidney injury status above and beyond proteinuria and renal function measurements alone. Statistical analyses across six DNA, protein, and metabolite markers were performed on a subset of residual spot urine samples with CKD that met assay performance quality controls from patients attending the clinical labs at the University of California, San Francisco (UCSF) as part of an ongoing IRB-approved prospective study. Inclusion criteria included selection of patients with confirmed CKD and normal healthy controls; exclusion criteria included incomplete or missing information for sample classification, logistical delays in transport/processing of urine samples or low sample volume, and acute kidney injury. Multivariate logistic regression of kidney injury status and likelihood ratio statistics were used to assess the contribution of the KIT Score for prediction of kidney injury status and stage of CKD as well as assess the potential contribution of the KIT Score for detection of early-stage CKD above and beyond traditional measures of renal function. Urine samples were processed by a proprietary immunoprobe for measuring cell-free DNA (cfDNA), methylated cfDNA, clusterin, CXCL10, total protein, and creatinine. The KIT Score and stratified KIT Score Risk Group (high versus low) had a sensitivity and specificity for detection of kidney injury status (healthy or CKD) of 97.3% (95% CI: 94.6–99.3%) and 94.1% (95% CI: 82.3–100%). In addition, in patients with normal renal function (estimated glomerular filtration rate (eGFR) ≥ 90), the KIT Score clearly identifies those with predisposing risk factors for CKD, which could not be detected by eGFR or proteinuria (p < 0.001). The KIT Score uncovers a burden of kidney injury that may yet be incompletely recognized, opening the door for earlier detection, intervention and preservation of renal function.

Background The Onco type DX® Prostate Cancer Assay is a multi-gene RT-PCR expression assay that was developed for use with fixed paraffin-embedded (FPE) diagnostic prostate needle biopsies containing as little as 1 mm of prostate tumor in the greatest dimension. The assay measures expression of 12 cancer-related genes representing four biological pathways and 5 reference genes which are algorithmically combined to calculate the Genomic Prostate Score (GPS). This biopsy-based assay has been analytically and subsequently clinically validated as a predictor of aggressive prostate cancer. The aim of this study was to validate the analytical performance of the Onco type DX Prostate Cancer Assay using predefined acceptance criteria. Results The lowest quartile of RNA yields from prostate needle biopsies (six 5 μm sections) was between 19 and 34 ng. Analytical validation of the process requiring as little as 5 ng of RNA met all pre-defined acceptance criteria. Amplification efficiencies, analytical sensitivity, and accuracy of gene assays were measured by serially diluting an RNA sample and analyzing features of the linear regression between RNA expression measured by the crossing point (Cp) versus the log 2 of the RNA input per PCR assay well. Gene assays were shown to accurately measure expression over a wide range of inputs (from as low as 0.005 ng to 320 ng). Analytical accuracy was excellent with average biases at qPCR inputs representative of patient samples <9.7% across all assays while amplification efficiencies were within ±6% of the median. Assessments of reproducibility and precision were performed by testing 10 prostate cancer RNA samples over multiple instruments, reagent lots, operators, days (precision), and RNA input levels (reproducibility) using appropriately parameterized linear mixed models. The standard deviations for analytical precision and reproducibility were 1.86 and 2.11 GPS units (100-unit scale) respectively . Conclusions The Onco type DX Prostate Cancer Assay, a clinical RT-PCR assay specifically designed for use with prostate needle biopsies, has been analytically validated using very limited RNA inputs. The assay requirements and analytical performance will provide physicians with test results from a robust and reliable assay which will enable improved treatment decisions for men diagnosed with early-stage prostate cancer.

Background: Oncotype DX™ is a clinically validated, high-complexity, multianalyte reverse transcription–PCR genomic test that predicts the likelihood of breast cancer recurrence in early-stage, node-negative, estrogen receptor–positive breast cancer. The Recurrence Score™ (RS) provides a more accurate, reproducible measure of breast cancer aggressiveness and therapeutic responsiveness than standard measures. Individualized patient management requires strict performance criteria for clinical laboratory tests. We therefore investigated the analytical performance of the assay. Methods: Assays used a pooled RNA sample from fixed paraffin-embedded tissues to evaluate the analytical performance of a 21-gene panel with respect to amplification efficiency, precision, linearity, and dynamic range, as well as limits of detection and quantification. Performance variables were estimated from assays carried out with sample dilutions. In addition, individual patient samples were used to test the optimized assay for reproducibility and sources of imprecision. Results: Assay results defined acceptable operational performance ranges, including an estimated maximum deviation from linearity of <1 cycle threshold (CT) units over a ≥2000-fold range of RNA concentrations, with a mean quantification bias of 0.3% and CVs of 3.2%–5.7%. An analysis of study design showed that assay imprecision contributed by instrument, operator, reagent, and day-to-day baseline variation was low, with SDs of <0.5 CT. Conclusion: The analytical and operational performance specifications defined for the Oncotype DX assay allow the reporting of quantitative RS values for individual patients with an SD within 2 RS units on a 100-unit scale.

BackgroundAlthough combination immune checkpoint inhibition and chemotherapy (ICI+C) is commonly used in patients with advanced non-small cell lung cancer (NSCLC), the benefit is variable. We have previously reported results from the myCare-040 study,1 where we validated an algorithm capable of distinguishing patients with favorable ICI+C benefit from those with no benefit. Using this algorithm, we have identified a set of genomic markers important for ICI+C benefit.MethodsThe algorithm (∆TRI) utilizes the Cellworks’ computational modeling of a patient’s tumor-specific genomic profile to predict disease and therapy directed alterations of biomarkers associated with ICI+C benefit. ∆TRI predicts the absolute increase in percentage likelihood of survival at 24 months for patients receiving ICI+C versus ICI alone. In the myCare-040 validation cohort, patients with predicted high ICI+C benefit (∆TRI > 16, n=73) realized a 8.3 month increase in median overall survival when receiving ICI+C versus ICI alone, whereas patients with a low score (∆TRI ≤16, n=255) showed no ICI+C benefit. We analyzed both benefit groups for significant enrichment (p < 0.05 by Fisher Exact test) of genomic aberrations conferring either sensitivity or resistance to ICI+C.ResultsOverall, 592 total genomic aberrations (SNVs and CNAs) were identified in the myCare-040 cohort, with 24 (4.1%) showing significant enrichment (p < 0.05) in either benefit group. Of this set, 3 aberrations were enriched in the no ICI+C benefit group (TP53-SOF, p < 0.0001, FDR < 0.0001; KRAS-SOF, p = 0.007; CD274 amplification, p = 0.006), with the remaining 21 aberrations showing enrichment in the ICI+C benefit group. Aberrations in ATM and ATR, two genes involved in DNA damage response, were among those showing the greatest enrichment in the ICI+C benefit group (ATM-LOF, p < 0.0001, FDR < 0.0001; ATR-LOF p = 0.003), highlighting the interaction between compromised DNA damage response and chemotherapy sensitivity.ConclusionsIn the myCare-040 cohort, most aberrations impacting ICI+C benefit were enriched in patients showing high ICI+C benefit, with some of these mutations possibly contributing to increased chemosensitivity by compromising DNA damage response. Notably, these aberrations were observed across both benefit groups, limiting their utility as biomarkers for directly predicting ICI+C efficacy and highlighting the benefit of a genomically integrative approach for determining ICI+C benefit.ReferenceAggarawal, et al. World Congress Lung Cancer, 2025.

We evaluated the analytical and clinical performance of a novel circulating tumor cell (CTC)-based blood test for determination of programmed death ligand 1 (PD-L1) protein expression status in real time in treatment-naïve non-small cell lung cancer (NSCLC) patients. CTCs were detected in 86% of patients with NSCLC (I–IV) at the time of diagnosis, with a 67% PD-L1 positivity rate (≥ 1 PDL + CTC). Among 33 NSCLC patients with PD-L1 results available via both tissue immunohistochemistry (IHC) and CTC assays, 78.9% were positive according to both methods. The CTC test identified an additional ten cases that were positive for PD-L1 expression but that tested negative via IHC analysis. Detection of higher PD-L1 expression on CTCs compared to that in the corresponding tissue was concordant with data obtained using other platforms in previously treated patients. The concordance in PD-L1 expression between tissue and CTCs was approximately 57%, which is higher than that reported by others. In summary, evaluation of PD-L1 protein expression status on CTCs isolated from NSCLC patients is feasible. PD-L1 expression status on CTCs can be determined serially during the disease course, thus overcoming the myriad challenges associated with tissue analysis.

Interspersed among the formal studies were not only the interaction with students from different universities, backgrounds, and philosophical persuasions, but also the immediately practical option of getting hands-on experience in conjunction with the formal. At the time of my involvement, most work at the lab centered on material retrieved from Kincaid in southern Illinois, sites that had occupied University of Chicago archaeology students for a number of years.