Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation.

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities.

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

Detecting Disruption of HER2 Membrane Protein Organization in Cell Membranes with Nanoscale Precision.

The spatiotemporal organization of proteins within the cell membrane can affect numerous biological functions, including cell signaling, communication, and transportation. Deviations from normal spatial arrangements have been observed in various diseases, and a better understanding of this process is a key stepping stone to advancing development of clinical interventions. However, given the nanometer length scales involved, detecting these subtle changes has primarily relied on complex super-resolution and single-molecule imaging methods. In this work, we demonstrate an alternative fluorescent imaging strategy for detecting protein organization based on a material that exhibits a unique photophysical behavior known as aggregation-induced emission (AIE). Organic AIE molecules have an increase in emission signal when they are in close proximity, and the molecular motion is restricted. This property simultaneously addresses the high background noise and low detection signal that limit conventional widefield fluorescent imaging. To demonstrate the potential of this approach, the fluorescent molecule sensor is conjugated to a human epidermal growth factor receptor 2 (HER2)-specific antibody and used to investigate the spatiotemporal behavior of HER2 clustering in the membrane of HER2-overexpressing breast cancer cells. Notably, the disruption of HER2 clusters in response to an FDA-approved monoclonal antibody therapeutic (Trastuzumab) is successfully detected using a simple widefield fluorescent microscope. While the sensor demonstrated here is optimized for sensing HER2 clustering, it is an easily adaptable platform. Moreover, given the compatibility with widefield imaging, the system has the potential to be used with high-throughput imaging techniques, accelerating investigations into membrane protein spatiotemporal organization.

KRAS Sequence Variation as Prognostic Marker in Patients With Young- vs Late-Onset Colorectal Cancer.

Importance: The understanding of the association between KRAS sequence variation status and clinical outcomes in colorectal cancer (CRC) has evolved over time. Objective: To characterize the association of age at onset, tumor sidedness, and KRAS sequence variation with survival among patients diagnosed with CRC. Design, Setting, and Participants: This cross-sectional study used data extracted from the Surveillance, Epidemiology, and End Results database. Patients diagnosed with adenocarcinoma of the colon or rectum from 2010 through 2015 were included and were classified as having young-onset (YO) cancer if diagnosed between ages 20 to 49 years and late-onset (LO) cancer if diagnosed at age 50 years or older. Data were analyzed from April 2021 through August 2023. Main Outcomes and Measures: CRC cause-specific survival (CSS) was summarized using Fine and Gray cumulative incidence and Kaplan-Meier curves. Estimation of subdistribution hazard ratios (sHRs) for the association of KRAS status, age at onset, and tumor location with CRC CSS was conducted using the Fine and Gray competing risk model. Cox proportional hazards regression was used to estimate and compare HRs. Results: Among 21 661 patients with KRAS sequence variation status (mean [SD] age at diagnosis, 62.50 [13.78] years; 9784 females [45.2%]), 3842 patients had YO CRC, including 1546 patients with KRAS variants, and 17 819 patients had LO CRC, including 7311 patients with KRAS variants. There was a significant difference in median CSS time between patients with variant vs wild-type KRAS (YO: 3.0 years [95% CI, 2.8-3.3 years] vs 3.5 years [95% CI, 3.3-3.9 years]; P = .02; LO: 2.5 years [95% CI, 2.4-2.7 years] vs 3.4 years [95% CI, 3.3-3.6 years]; P < .001). Tumors with variant compared with wild-type KRAS were associated with higher risk of CRC-related death (YO: sHR, 1.09 [95% CI, 1.01-1.18]; P = .03; LO: sHR, 1.06 [95% CI, 1.02-1.09]; P = .002). Among patients with YO cancer, mortality hazards increased by location, from right (sHR, 1.02 [95% CI, 0.88-1.17) to left (sHR, 1.15 [95% CI, 1.02-1.29) and rectum (sHR, 1.16 [95% CI, 0.99-1.36), but no trend by tumor location was seen for LO cancer. Conclusions and Relevance: In this study of patients diagnosed with CRC, KRAS sequence variation was associated with increased mortality among patients with YO and LO tumors. In YO cancer, variant KRAS-associated mortality risk was higher in distal tumors than proximal tumors.

Trends in Epidemiology of Esophageal Cancer in the US, 1975-2018.

Importance: Esophageal cancer (EC) is the 7th most common cancer worldwide and 14th in the US. More data are needed to study the changing incidence patterns of its 2 primary histologic subtypes, squamous cell carcinoma of the esophagus (SCE) and adenocarcinoma of the esophagus (ACE). Objective: To examine temporal trends in incidence rates of EC, ACE, and SCE from 1975 through 2018. Design, Setting, and Participants: In this population-based cross-sectional study, data were derived from 9 Surveillance, Epidemiology, and End Results (SEER) registries from January 1975 through December 2018 and from all 21 registries for January 2000 through December 2018 for patients with a diagnosis of EC from 1975 through 2018 (International Classification of Disease-Oncology, Third Edition codes). Age-adjusted incidence rates (AAIRs) of EC, ACE, and SCE were calculated. The timing and magnitude of the annual percentage change (APC) in incidence were examined using Joinpoint regression analyses. Data analysis was started in 2021 and updated and completed in 2023. Main Outcome and Measures: The APC for age-adjusted EC incidence rates as stratified by histology, anatomical location, stage, sex, age, race and ethnicity, and geographic region. Results: A total of 47 648 patients with a diagnosis of EC were retained for analysis. These included 22 419 (47.1%) with a diagnosis of SCE, 22 217 (46.6%) with ACE, and 3012 (6.3%) with other subtypes. The AAIR for EC changed from 4.14 per 100 000 population in 1975 to 4.18 in 2018, AAIRs of SCE declined from 3.06 in 1975 to 1.15 in 2018 as well as for ACE, and AAIRs increased from 0.42 in 1975 to 2.78 in 2018. From 1975 through 2004, EC incidence significantly increased (APC, 0.53; 95% CI, 0.4 to 0.7) but significantly decreased (APC, -1.03; 95% CI, -1.3 to -0.7) from then until 2018. The APC of SCE significantly continued to decline (-2.80, 95% CI, -3.0 to -2.6), and ACE increased from 2000 to 2006 (APC, 2.51; 95% CI, 1.0 to 4.0) but has since stabilized from 2006 to 2018. Conclusions and Relevance: The results of this cross-sectional study suggest that the incidence of EC modestly declined since 2004 and that the incidence of SCE continued to decline while the incidence rate of ACE plateaued for more than a decade. Understanding factors associated with plateaued rates of ACE may help inform public health interventions.

Chemotherapy Treatment Costs and Clinical Outcomes of Colon Cancer in the U.S. Military Health System's Direct and Private Sector Care Settings.

INTRODUCTION: Identifying low-value cancer care may be an important step in containing costs associated with treatment. Low-value care occurs when the medical services, tests, or treatments rendered do not result in clinical benefit. These may be impacted by care setting and patients' access to care and health insurance. We aimed to study chemotherapy treatment and the cost paid by the Department of Defense (DoD) for treatment in relation to clinical outcomes among patients with colon cancer treated within the U.S. Military Health System's direct and private sector care settings to better understand the value of cancer care. MATERIALS AND METHODS: A cohort of patients aged 18 to 64 years with primary colon cancer diagnosed between January 1, 1999, and December 31, 2014, were identified in the Military Cancer Epidemiology database. Multivariable time-dependent Cox proportional hazards regression models were used to assess the relationship between chemotherapy treatment and the cost paid by the DoD (in quartiles, Q) and the outcomes of cancer progression, cancer recurrence, and all-cause death modeled as adjusted hazard ratios (aHRs) and 95% confidence intervals (95% CIs). The Military Cancer Epidemiology data were approved for research by the Uniformed Services University of the Health Sciences' Institutional Review Board. RESULTS: The study included 673 patients using direct care and 431 patients using private sector care. The median per patient chemotherapy costs in direct care ($111,202) were lower than in private sector care ($350,283). In direct care, higher chemotherapy costs were associated with an increased risk of any outcome but not with all-cause death. In private sector care, higher chemotherapy costs were associated with a higher risk of any outcome and with all-cause death (aHR, 2.67; 95% CI, 1.20-5.92 for Q4 vs. Q1). CONCLUSIONS: The findings in the private sector may indicate low-value care in terms of the cost paid by the DoD for chemotherapy treatment and achieving desirable survival outcomes for patients with colon cancer in civilian health care. Comprehensive evaluations of value-based care among patients treated for other tumor types may be warranted.

An Inflection Point in Cancer Protein Biomarkers: What was and What's Next.

Biomarkers remain the highest value proposition in cancer medicine today-especially protein biomarkers. Despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system, and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last 2 decades have seen an explosion of multiomics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single-cell analysis, artificial intelligence (machine and deep learning) for data analysis, and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking toward viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to redefine biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Contrived Materials and a Data Set for the Evaluation of Liquid Biopsy Tests: A Blood Profiling Atlas in Cancer (BLOODPAC) Community Study.

The Blood Profiling Atlas in Cancer (BLOODPAC) Consortium is a collaborative effort involving stakeholders from the public, industry, academia, and regulatory agencies focused on developing shared best practices on liquid biopsy. This report describes the results from the JFDI (Just Freaking Do It) study, a BLOODPAC initiative to develop standards on the use of contrived materials mimicking cell-free circulating tumor DNA, to comparatively evaluate clinical laboratory testing procedures. Nine independent laboratories tested the concordance, sensitivity, and specificity of commercially available contrived materials with known variant-allele frequencies (VAFs) ranging from 0.1% to 5.0%. Each participating laboratory utilized its own proprietary evaluation procedures. The results demonstrated high levels of concordance and sensitivity at VAFs of >0.1%, but reduced concordance and sensitivity at a VAF of 0.1%; these findings were similar to those from previous studies, suggesting that commercially available contrived materials can support the evaluation of testing procedures across multiple technologies. Such materials may enable more objective comparisons of results on materials formulated in-house at each center in multicenter trials. A unique goal of the collaborative effort was to develop a data resource, the BLOODPAC Data Commons, now available to the liquid-biopsy community for further study. This resource can be used to support independent evaluations of results, data extension through data integration and new studies, and retrospective evaluation of data collection.

Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways.

We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.

Translating "Big Data" in Oncology for Clinical Benefit: Progress or Paralysis.

The molecular characterization of cancer through genomics, data from multiomics technologies, molecular-driven clinical trials, and internet-enabled devices capturing patient context and real-world data are creating an unprecedented big data revolution across the cancer research-care continuum. While big data has translated to benefit for some patients, it has also created new problems. Our intent in this brief communication is to explore some examples of progress and key challenges that remain. The problems are not intractable, but success will require rethinking and rebuilding an information and evidence-based learning system that moves beyond paralysis to shape a better future for patients with cancer.

Adaptation of Imaging Mass Cytometry to Explore the Single Cell Alloimmune Landscape of Liver Transplant Rejection.

Rejection continues to be an important cause of graft loss in solid organ transplantation, but deep exploration of intragraft alloimmunity has been limited by the scarcity of clinical biopsy specimens. Emerging single cell immunoprofiling technologies have shown promise in discerning mechanisms of autoimmunity and cancer immunobiology. Within these applications, Imaging Mass Cytometry (IMC) has been shown to enable highly multiplexed, single cell analysis of immune phenotypes within fixed tissue specimens. In this study, an IMC panel of 10 validated markers was developed to explore the feasibility of IMC in characterizing the immune landscape of chronic rejection (CR) in clinical tissue samples obtained from liver transplant recipients. IMC staining was highly specific and comparable to traditional immunohistochemistry. A single cell segmentation analysis pipeline was developed that enabled detailed visualization and quantification of 109,245 discrete cells, including 30,646 immune cells. Dimensionality reduction identified 11 unique immune subpopulations in CR specimens. Most immune subpopulations were increased and spatially related in CR, including two populations of CD45+/CD3+/CD8+ cytotoxic T-cells and a discrete CD68+ macrophage population, which were not observed in liver with no rejection (NR). Modeling via principal component analysis and logistic regression revealed that single cell data can be utilized to construct statistical models with high consistency (Wilcoxon Rank Sum test, p=0.000036). This study highlights the power of IMC to investigate the alloimmune microenvironment at a single cell resolution during clinical rejection episodes. Further validation of IMC has the potential to detect new biomarkers, identify therapeutic targets, and generate patient-specific predictive models of clinical outcomes in solid organ transplantation.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Potential association between public medical insurance, waitlist mortality, and utilization of living donor liver transplantation: An analysis of the Scientific Registry of Transplant Recipients.

BACKGROUND: The Affordable Care Act (ACA) and subsequent Medicaid expansion has increased utilization of public health insurance. Living donor liver transplantation (LDLT) increases access to transplant and is associated with improved survival but consistently represents < 5% of LT in the United States. STUDY DESIGN: National registry data were analyzed to evaluate the impact of insurance payor on waitlist mortality and LDLT rates at LDLT centers since implementation of the ACA. RESULTS: Public insurance [Medicare RR 1.18 (1.13-1.22) P < .001, Medicaid RR 1.22 (1.18-1.27) P < .001], Latino ethnicity (P < .001), and lower education level (P = .02) were associated with increased waitlist mortality at LDLT centers. LDLT recipients were more likely to have private insurance (70.4% vs. 59.4% DDLT, P < .001), be Caucasian (92.1% vs. 83% DDLT, P < .001), and have post-secondary education (66.8% vs. 54.1% DDLT, P < .001). Despite 78% of LDLT centers being located in states with Medicaid expansion, there was no change in LDLT utilization among recipients with Medicaid (P = .196) or Medicare (P = .273). CONCLUSION: Despite Medicaid expansion, registry data suggests that patients with public medical insurance may experience higher waitlist mortality and underutilize LDLT at centers offering LDLT. It is possible that Medicaid expansion has not increased access to LDLT.

BloodPAC Data Commons for Liquid Biopsy Data.

PURPOSE: The Blood Profiling Atlas in Cancer (BloodPAC) Data Commons (BPDC) is being developed and is operated by the public-private BloodPAC Consortium to support the liquid biopsy community. It is an interoperable data commons with the ultimate aim of serving as a recognized source of valid scientific evidence for liquid biopsy assays for industry, academia, and standards and regulatory stakeholders. METHODS: The BPDC is implemented using the open source Gen3 data commons platform (https://gen3.org). In particular, the BPDC Data Exploration Portal, BPDC Data Submission Portal, the BPDC Workspace Hub, and the BloodPAC application programming interface (API) were all automatically generated from the BloodPAC Data Model using the Gen3 data commons platform. BPDC uses Gen3's implementation of the data commons framework services so that it can interoperate through secure, compliant APIs with other data commons using data commons framework service, such as National Cancer Institute's Cancer Research Data Commons. RESULTS: The BPDC contains 57 studies and projects spanning more than 4,100 cases. This amounts to 5,700 aliquots (blood plasma, serum, or a contrived sample) that have been subjected to a liquid biopsy assay, quantified, and then contributed by members of the BloodPAC Consortium. In all, there are more than 31,000 files in the commons as of December 2020. We describe the BPDC, the data it manages, the process that the BloodPAC Consortium used to develop it, and some of the applications that have been developed using its API. CONCLUSION: The BPDC has been the data platform used by BloodPAC during the past 4 years to manage the data for the consortium and to provide workspaces for its working groups.

Paradoxical androgen receptor regulation by small molecule enantiomers.

Small molecules that target the androgen receptor (AR) are the mainstay of therapy for lethal castration-resistant prostate cancer (CRPC), yet existing drugs lose their efficacy during continued treatment. This evolution of resistance is due to heterogenous mechanisms which include AR mutations causing the identical drug to activate instead of inhibit the receptor. Understanding in molecular detail the paradoxical phenomenon wherein an AR antagonist is transformed into an agonist by structural mutations in the target receptor is thus of paramount importance. Herein, we describe a reciprocal paradox: opposing antagonist and agonist AR regulation determined uniquely by enantiomeric forms of the same drug structure. The antiandrogen BMS-641988, which has (R)-chirality at C-5 encompasses a previously uncharacterized (S)-stereoisomer that is, surprisingly, a potent agonist of AR, as demonstrated by transcriptional assays supported by cell imaging studies. This duality was reproduced in a series of novel compounds derived from the BMS-641988 scaffold. Coupled with in silico modeling studies, the results inform an AR model that explains the switch from potent antagonist to high-affinity agonist in terms of C-5 substituent steric interactions with helix 12 of the ligand binding site. They imply strategies to overcome AR drug resistance and demonstrate that insufficient enantiopurity in this class of AR antagonist can confound efforts to correlate structure with function.

Real-World Outcomes for Patients Treated With Immune Checkpoint Inhibitors in the Veterans Affairs System.

PURPOSE: Increasingly broad patient groups are being treated with immune checkpoint inhibitors (ICIs) in clinical practice, but few studies have assessed their usage and outcomes in large, comprehensive real-world cohorts. We identified patients who received ICIs in the Veterans Affairs (VA) health care system and described patient characteristics and survival outcomes across multiple indications. METHODS: We conducted a retrospective analysis using electronic health record data from VA facilities nationwide. Overall survival (OS) from time of ICI initiation for key indications was estimated by Kaplan-Meier. We also stratified OS by frailty status, as defined by a surrogate index developed in VA data. For select indications, we further compared outcomes to historic and concurrent control patients treated with standard-of-care regimens at the VA. RESULTS: We identified 11,888 patients who were treated with ICIs and determined the cancer type and indication for which they were treated. The cohort is enriched for patient groups that are under-represented in pivotal clinical trials (PCTs), including older, non-White, and/or higher disease burdened patients. Generally, OS observed in the VA cohort is lower than that reported in PCTs. After stratifying VA patients by frailty status, OS among nonfrail patients is more similar to OS reported in PCTs for some indications. Compared with internal VA control cohorts, patients treated with ICIs generally exhibited longer OS for all indications considered. CONCLUSION: This study describes ICI outcomes across multiple tumor types in a real-world population at the VA. For most indications, real-world survival outcomes are observed to be lower than those reported in PCTs, but patients receiving ICIs still achieve longer survival relative to patients receiving standard of care.

Use of Wearable Activity Tracker in Patients With Cancer Undergoing Chemotherapy: Toward Evaluating Risk of Unplanned Health Care Encounters.

PURPOSE: Unplanned health care encounters (UHEs) such as emergency room visits can occur commonly during cancer chemotherapy treatments. Patients at an increased risk of UHEs are typically identified by clinicians using performance status (PS) assessments based on a descriptive scale, such as the Eastern Cooperative Oncology Group (ECOG) scale. Such assessments can be bias prone, resulting in PS score disagreements between assessors. We therefore propose to evaluate PS using physical activity measurements (eg, energy expenditure) from wearable activity trackers. Specifically, we examined the feasibility of using a wristband (band) and a smartphone app for PS assessments. METHODS: We conducted an observational study on a cohort of patients with solid tumor receiving highly emetogenic chemotherapy. Patients were instructed to wear the band for a 60-day activity-tracking period. During clinic visits, we obtained ECOG scores assessed by physicians, coordinators, and patients themselves. UHEs occurring during the activity-tracking period plus a 90-day follow-up period were later compiled. We defined our primary outcome as the percentage of patients adherent to band-wear ≥ 80% of 10 am to 8 pm for ≥ 80% of the activity-tracking period. In an exploratory analysis, we computed hourly metabolic equivalent of task (MET) and counted 10 am to 8 pm hours with > 1.5 METs as nonsedentary physical activity hours. RESULTS: Forty-one patients completed the study (56.1% female; 61.0% age 40-60 years); 68% were adherent to band-wear. ECOG score disagreement between assessors ranged from 35.3% to 50.0%. In our exploratory analysis, lower average METs and nonsedentary hours, but not higher ECOG scores, were associated with higher 150-day UHEs. CONCLUSION: The use of a wearable activity tracker is generally feasible in a similar population of patients with cancer. A larger randomized controlled trial should be conducted to confirm the association between lower nonsedentary hours and higher UHEs.

Generic Protocols for the Analytical Validation of Next-Generation Sequencing-Based ctDNA Assays: A Joint Consensus Recommendation of the BloodPAC's Analytical Variables Working Group.

Liquid biopsy, particularly the analysis of circulating tumor DNA (ctDNA), has demonstrated considerable promise for numerous clinical intended uses. Successful validation and commercialization of novel ctDNA tests have the potential to improve the outcomes of patients with cancer. The goal of the Blood Profiling Atlas Consortium (BloodPAC) is to accelerate the development and validation of liquid biopsy assays that will be introduced into the clinic. To accomplish this goal, the BloodPAC conducts research in the following areas: Data Collection and Analysis within the BloodPAC Data Commons; Preanalytical Variables; Analytical Variables; Patient Context Variables; and Reimbursement. In this document, the BloodPAC's Analytical Variables Working Group (AV WG) attempts to define a set of generic analytical validation protocols tailored for ctDNA-based Next-Generation Sequencing (NGS) assays. Analytical validation of ctDNA assays poses several unique challenges that primarily arise from the fact that very few tumor-derived DNA molecules may be present in circulation relative to the amount of nontumor-derived cell-free DNA (cfDNA). These challenges include the exquisite level of sensitivity and specificity needed to detect ctDNA, the potential for false negatives in detecting these rare molecules, and the increased reliance on contrived samples to attain sufficient ctDNA for analytical validation. By addressing these unique challenges, the BloodPAC hopes to expedite sponsors' presubmission discussions with the Food and Drug Administration (FDA) with the protocols presented herein. By sharing best practices with the broader community, this work may also save the time and capacity of FDA reviewers through increased efficiency.

Quantified Kinematics to Evaluate Patient Chemotherapy Risks in Clinic.

PURPOSE: Performance status (PS) is a key factor in oncologic decision making, but conventional scales used to measure PS vary among observers. Consumer-grade biometric sensors have previously been identified as objective alternatives to the assessment of PS. Here, we investigate how one such biometric sensor can be used during a clinic visit to identify patients who are at risk for complications, particularly unexpected hospitalizations that may delay treatment or result in low physical activity. We aim to provide a novel and objective means of predicting tolerability to chemotherapy. METHODS: Thirty-eight patients across three centers in the United States who were diagnosed with a solid tumor with plans for treatment with two cycles of highly emetogenic chemotherapy were included in this single-arm, observational prospective study. A noninvasive motion-capture system quantified patient movement from chair to table and during the get-up-and-walk test. Activity levels were recorded using a wearable sensor over a 2-month period. Changes in kinematics from two motion-capture data points pre- and post-treatment were tested for correlation with unexpected hospitalizations and physical activity levels as measured by a wearable activity sensor. RESULTS: Among 38 patients (mean age, 48.3 years; 53% female), kinematic features from chair to table were the best predictors for unexpected health care encounters (area under the curve, 0.775 ± 0.029) and physical activity (area under the curve, 0.830 ± 0.080). Chair-to-table acceleration of the nonpivoting knee (t = 3.39; P = .002) was most correlated with unexpected health care encounters. Get-up-and-walk kinematics were most correlated with physical activity, particularly the right knee acceleration (t = -2.95; P = .006) and left arm angular velocity (t = -2.4; P = .025). CONCLUSION: Chair-to-table kinematics are good predictors of unexpected hospitalizations, whereas the get-up-and-walk kinematics are good predictors of low physical activity.