PSA/testosterone ratio as a potential biomarker to identify early progressors of adaptive therapy in metastatic castration sensitive prostate cancer
Gallaher, J. A.; Gatenby, R. A.; Brown, J.; Anderson, A. R. A.; Zhang, J.
Show abstract
PURPOSEWe aim to identify biomarkers of progression in an ongoing pilot trial using adaptive dosing in metastatic castrate sensitive prostate cancer (mCSPC). PATIENTS AND METHODSMen with mCSPC were given combined androgen deprivation therapy with an androgen receptor signaling inhibitor followed by a treatment break after achieving >75% PSA decline. This was followed by evolution-informed drug cycling to prevent resistance outgrowth. Just 6 of 16 patients have progressed at month 55, and we wish to identify predictive features that would help identify these patients. We compare clinical features, testosterone metrics, and PSA metrics over the trial cohort, and we fit a mathematical model to the dynamic patient-specific data to gain more mechanistic insight as to what might be driving early progression. RESULTSSignificant clinical risk factors predicting progression included the risk status, the number of bone metastases, and the total number of metastases. However, analyses of the dynamical changes in PSA and testosterone during the initial cycle of treatment identified several useful features to discriminate early, late, and non-progressors. Specifically, a higher PSA at the end of the induction phase, a lower percent change in PSA over the induction period, and a higher average PSA/Testosterone ratio over the first cycle significantly predicted progressors. Thresholds for these metrics are able to distinguish the earliest progressors. A simple mathematical model coupling the PSA and the testosterone dynamics suggests a higher fraction of resistant cells exist prior to therapy for those that progress earlier. CONCLUSIONPSA/Testosterone ratio is a useful feature to couple drug response to tumor burden dynamics and predict early progression from the first cycle of adaptive therapy.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- High accuracy indicators of androgen suppression therapy failure for prostate cancer – a modeling study 96%
- Modifying adaptive therapy to enhance competitive suppression 96%
- Standing Variations Modeling Captures Inter-Individual Heterogeneity in a Deterministic Model of Prostate Cancer Response to Combination Therapy 92%
Similar papers in this journal
- Chemotherapy-Induced Cachexia and Model-Informed Dosing to Preserve Lean Mass in Cancer Treatment 92%
- Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy 92%
- A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response 91%
Similar papers in this journal
- Multidisciplinary analysis of evolution based Abiraterone treatment for metastatic castrate resistant prostate cancer 96%
- Patient-specific Boolean models of signaling networks guide personalized treatments 93%
- Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling 91%
Similar papers in this journal
- Accurate prognosis for localized prostate cancer through coherent voting networks and multi-omic data 93%
- Migrastatic Therapy as a Potential Game-Changer inAdaptive Cancer Treatment 91%
- CD117/c-kit Represents a Prostate Cancer Stem-Like Subpopulation Driving Progression, Migration, and TKI Resistance 90%
Similar papers in this journal
- Osthole Suppresses Prostate Cancer Progression by Modulating PRLR and the JAK2/STAT3 Signaling Axis 90%
- Syngeneic model of carcinogen-induced tumor mimics basal/squamous, stromal-rich, and neuroendocrine molecular and immunological features of muscle-invasive bladder cancer 90%
- 5hmC-profiles in Puerto Rican Hispanic/Latino men with aggressive prostate cancer 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.