Lesli A. Kiedrowski

Plasma cell-free DNA (cfDNA) is a sensitive, noninvasive tool for monitoring the genomic landscape, heterogeneity, and evolution of cancer, aggregating genetic information from multiple metastatic sites. Here, we study 15,564 cfDNA samples from 12,827 patients with metastatic breast cancer (MBC). We classify samples, deep-sequenced with Guardant360, into MBC subtypes using our trained random forest model. Most patients have variants associated with therapeutic resistance and targetability of approved and experimental therapies, including in PIK3CA, ESR1, PTEN , and AKT1 . Frequencies of multiple actionable alterations differed from primary disease, and multiple variants per gene suggest independent resistant clones. The large cohort size enables uncovering clonal co-behaviors, including co-existence and exclusivity of alleles and correlation of allele fractions. cfDNA testing is now recommended in multiple advanced malignancies, including eligibility assessment for clinical trials. We envision this large collection of MBC cfDNA data as an invaluable resource for the design and planning of future clinical trials.

PURPOSE: Metastatic hormone-sensitive prostate cancer (mHSPC) is a heterogeneous disease state with multiple treatment options. Biomarkers are needed for risk stratification and to guide treatment intensification or deintensification. We used a computational histology artificial intelligence–based platform (CHAI) to develop and validate a digital image–only prognostic biomarker in mHSPC with participant-level data from two prospective, phase III randomized controlled trials. METHODS: The CHAI platform extracted quantitative histomorphologic features from whole-slide images of hematoxylin and eosin–stained diagnostic specimens. Data from CHAARTED were used to construct a signature of features associated with overall survival (OS). A continuous risk score was dichotomized into favorable- and unfavorable-risk groups. The performance of the locked model was assessed in ENZAMET as an independent validation cohort using Kaplan-Meier methods and multivariable Cox proportional hazards models. Institutional review board approval was obtained for each participating data set. Given all patient information was deidentified, the study was considered institutional review board–exempt and consent waived. RESULTS: Overall, 1,191 participants were included: 507 in development (CHAARTED) and 684 in validation (ENZAMET). In the validation cohort, CHAI unfavorable-risk participants had worse OS (hazard ratio [HR], 2.6 [95% CI, 2.0 to 3.4]; P < .001) and progression-free survival (PFS; HR, 2.2 [95% CI, 1.7 to 2.7]; P < .001). Five-year OS was 39% for unfavorable-risk versus 69% for favorable-risk participants. In adjusting for clinical variables, the biomarker remained prognostic for OS and PFS ( P < .001). Among those who received doublet (androgen-deprivation therapy [ADT] plus androgen receptor pathway inhibitor [ARPI]) versus triplet systemic therapy (ADT plus docetaxel plus ARPI), there was a significant interaction between biomarker and treatment intensification for OS ( P = .003) and PFS ( P < .001), suggesting that patients at higher risk may benefit more from triplet therapy. CONCLUSION: We developed and validated an image-only AI-based biomarker associated with clinical outcomes and potential benefit from treatment escalation in mHSPC independent of conventional clinicopathologic risk factors.

PURPOSE: Predictive biomarkers to guide selection of first-line chemotherapy for advanced pancreatic ductal adenocarcinoma (PDAC) are an unmet clinical need. This study used the Computational Histology Artificial Intelligence (CHAI) platform to develop and validate a histomorphology-based G-chemo versus F-chemo (GvF) biomarker that predicts benefit from first-line fluoropyrimidine-based (F-chemo) versus gemcitabine-based (G-chemo) regimens. METHODS: The CHAI platform extracted quantitative histomorphologic features from whole-slide images of hematoxylin and eosin–stained diagnostic biopsies. In a multi-institutional development cohort, features associated with differential outcomes as measured by time to next treatment or death (TNTD) between F-chemo–treated and G-chemo–treated patients produced continuous biomarker scores, which were dichotomized into G-pref or F-pref results. The biomarker and threshold were locked. An independent validation cohort from the prospective COMPASS and Know Your Tumor studies assessed differential treatment outcomes by TNTD and overall survival (OS). RESULTS: There were 477 patients (development: 178; validation: 299). In validation, among 173 F-pref patients, those treated with F-chemo had significantly better outcomes than G-chemo for both TNTD ( P = .035; median TNTD: F-chemo 8.6 months; G-chemo 7.5 months) and OS ( P = .003; median OS: F-chemo 14.4 months; G-chemo 11.7 months). Among 126 G-pref patients, G-chemo had significantly superior TNTD ( P = .038; median TNTD: F-chemo 7.2 months; G-chemo 9.6 months), but no difference in OS ( P = .5; median OS: F-chemo 12.4 months; G-chemo 14.3 months). In propensity score–weighted analysis, the biomarker predicted treatment effect (biomarker-treatment interaction TNTD P < .001; OS P = .005). RNA subtypes were associated with TNTD and OS but did not predict differential treatment effects ( P = .3). CONCLUSION: The histomorphology-based GvF biomarker predicted differential treatment benefit of first-line GvF. This biomarker can guide optimal treatment selection for first-line therapy in advanced PDAC.

Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) management is increasingly complex due to conflicting guideline-based risk classifications, ongoing Bacillus Calmette–Guérin (BCG) shortages, and emerging alternative therapies. Computational Histology Artificial Intelligence (CHAI) tests are clinically available, providing insights from tumor specimens including predicting BCG responsiveness and individualized recurrence and progression risks, which may support precision medicine. This technology features biomarkers purpose-built for clinically unmet needs and has practical advantages including a fast turnaround time and no need for consumption of tissue or other specimens. We assessed the impact of such tests on physicians’ decision-making in routine, real-world NMIBC management. Methods: Physicians at six centers ordered CHAI tests (Vesta Bladder) at their discretion during routine NMIBC care. Tumor specimens were processed by a CLIA/CAP-accredited laboratory (Valar Labs, Houston, TX, USA) where H&E-stained slides were analyzed with the CHAI assay to extract histomorphic features of the tumor and microenvironment, which were algorithmically assessed to generate biomarker test results. For each case from 24 June 2024 to 18 July 2025, ordering physicians were surveyed to assess pre- and post-test management plans and post-test result usefulness. Results: Among 105 high-grade NMIBC cases with complete survey results available, primary management changed in 67% (70/105). Changes included modality shifts (n = 7; three to radical cystectomy with high prognostic risk scores; four avoiding cystectomy with low scores) and intravesical agent change (n = 63). Surveillance was intensified in 7%, predominantly among those with ≥90th percentile risk scores. The therapeutic agent changed in 80% (40/50) of predictive biomarker-present (indicative of poor response to BCG) tumors vs. 48% (23/48) of biomarker-absent tumors. Conclusions: In two thirds of cases, CHAI biomarker results influenced clinical decision-making during routine care. BCG predictive biomarker results frequently guided intravesical agent selection. These results have implications for optimizing clinical outcomes, especially in the setting of ongoing BCG shortages. Prognostic risk stratification results guided treatment escalation vs. de-escalation, including surveillance intensification and surgical vs. bladder-sparing decisions. CHAI biomarkers are currently utilized in routine clinical care and informing precision NMIBC management.

Patient-Level Data for Patients with Reversion Mutations

ROC and Test Characteristics from VAF Analysis with Binary Outcome of 12-month OS

VAF association with 12 month OS

Representativeness of Study Participants