Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction

Abstract

Accurate prediction of long-term outcomes remains a challenge in the care of cancer patients. Due to the difficulty of serial tumor sampling, previous prediction tools have focused on pretreatment factors. However, emerging non-invasive diagnostics have increased opportunities for serial tumor assessments. We describe the Continuous Individualized Risk Index (CIRI), a method to dynamically determine outcome probabilities for individual patients utilizing risk predictors acquired over time. Similar to "win probability" models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient's course. Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models. We demonstrate CIRI's broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection. We envision that dynamic risk assessment will facilitate personalized medicine and enable innovative therapeutic paradigms.

Keywords: biomarkers; cancer; liquid biopsy; personalized medicine; predictive modeling.

Conflict of interest statement

Declaration of Interests

Olivier Casasnovas served as a consultant for Roche, Takeda, Gilead, BMS, MSD, Janssen and received research support from Roche, Gilead, and Takeda, outside the submitted work.

Anton W. Langerak is a member of the AbbVie advisory board and received research support from Roche-Genentech and Gilead, outside the submitted work.

Matthias Ritgen is a member of the Roche advisory board, outside the submitted work.

Sebastian Böttcher received research support and personal fees from AbbVie, Janssen, and F Hoffman-La Roche; research support from Celgene and Genentech; and personal fees from Becton Dickinson and Novartis, outside the submitted work.

Davide Rossi received research support from Gilead, Janssen, Roche, and AbbVie, outside the submitted work.

Michael Hallek received research support from Roche, Gilead, Mundipharma, Janssen, Celgene, Pharmacyclics, and AbbVie, outside the submitted work.

Maximilian Diehn served as a consultant for Roche, AstraZeneca, Novartis, BioNTech, and Quanticell, and received research support from Varian Medical Systems, outside the submitted work.

Ash Alizadeh served as a consultant for Roche, Genentech, Janssen, Pharmacyclics, Gilead, Celgene, and Chugai, and received research support from Pfizer, outside the submitted work. He is also a shareholder in FortySeven.

Ash Alizadeh, Maximillian Diehn, and Aaron Newman are co-founders of CiberMed.

The other authors declare no competing interests.

Copyright © 2019. Published by Elsevier Inc.

Figures

Figure 1.. Design and motivation for development of the Continuous Individualized Risk Index.

A) Patients with malignancies have a number of prognostic risk factors that can be evaluated throughout a course of therapy. CIRI integrates these to make a personalized prediction of treatment outcome. We describe two approaches; i) an initial naïve Bayes Approach, which produces the probability of event by a fixed primary endpoint, and ii) a Bayesian proportional hazards approach, which produces a prediction of survival at any future time-point. B) The course of a typical patient with DLBCL through therapy and surveillance is shown. Pretreatment risk factors (shown in blue) are obtained directly prior to therapy. Interim risk factors (shown in orange) are obtained early on during a course of therapy before the eventual clinical outcome. C) A graphical schema depicting CIRI-DLBCL is shown. The probability of achieving a desired clinical outcome (here, EFS24) at a given point in time is shown. As more information is obtained, this probability is updated. For example, both patient A (top) and B (bottom) have the same pretreatment risk factors. However, at the start of cycle 2, patient A has achieved a favorable EMR - therefore, her probability of reaching EFS24 increases. In contrast, patient B does not achieve an EMR at the start of cycle 2 - therefore, his probability of reaching EFS24 decreases. Similar changes are seen when MMR and interim PET/CT scans become available. D) A bar plot demonstrates the C-Statistic and 95% C.I. for predicting EFS24 by the IPI, all pretreatment factors, EMR, MMR, interim PET/CT, and CIRI; predictions from CIRI-DLBCL are made after integration of all data. * indicates P

Figure 2.. Extension of CIRI to survival data.

A) A schema for extending CIRI-DLBCL to patient-specific survival curve prediction. Similar to Fig 1C, the predicted survival function for a given patient is updated as more information becomes available. The survival curves are shown with 80% C.I.; predictions from the time-point specific CIRI-DLBCL in Fig 1 are also shown. B) Calibration plot of CIRI-DLBCL including predictions from all time-points, demonstrating predicted EFS24 (x-axis) and observed EFS24 (y-axis). See STAR Methods (Determination of model calibration) for details. The dotted line shows predicted versus observed risk (moving average) of event within 24 months. Patients were also grouped into deciles by predicted risk. The observed risk of each decile was calculated by the Kaplan-Meier method (blue dots). Areas of under and over prediction of risk by >10% are shown in red and green. Further calibration statistics are provided in the legend. C) Bar plots demonstrate the C-Statistic and 95% C.I. for predicting EFS at multiple time intervals by the IPI, all pretreatment risk factors, EMR, MMR, interim PET/CT, and CIRI; predictions from CIRI-DLBCL are made after integration of all data. D) A Kaplan-Meier estimate shows the EFS for patients stratified by CIRI-DLBCL risk-prediction of EFS24 split into three groups after integration of all information (i.e., at cycle 4, after interim PET/CT scan). E) Similar to panel D, a Kaplan-Meier estimate shows the event-free survival for patients stratified by CIRI-DLBCL into three groups. Here, risk-predictions across all time-points are shown. * indicates P < 0.05. See also Figure S2.

Figure 3.. CIRI applied to chronic lymphocytic leukemia.

A) The course of a patient with CLL through therapy and surveillance is shown. Pretreatment risk factors (shown in blue) are obtained directly prior to therapy. Interim and end of therapy risk factors (shown in orange and purple) are obtained throughout a course of therapy, before the eventual clinical outcome (PFS36). B) Similar to Fig 2B, the displayed plot demonstrates the calibration of predictions from CIRI-CLL with observed patient outcomes across predictions from all time-points. C) Bar plots demonstrates the C-Statistic and 95% C.I. for predicting progression-free survival at various time-points using the CLL-IPI, all pretreatment risk factors, interim MRD, end of therapy MRD, and CIRI-CLL. Predictions from CIRI are made after integration of all data. D) A Kaplan-Meier estimate shows the PFS for patients stratified by CIRI-CLL risk-prediction of PFS36 split into three groups at the time of final restaging after integration of all information (i.e., at the end of therapy with knowledge of final MRD). To guard against guaranteed time bias, patients with progression prior to end of therapy MRD assessment (n=35, 5%) were excluded. E) Similar to panel D, a Kaplan-Meier estimate shows the PFS for patients stratified by CIRI-CLL. Here, risk-predictions across all time-points are shown; as many more individual risk-predictions are available, patients are split into 10-strata to demonstrate the power of this approach and model calibration at the desired endpoint (PFS at 36 months). To guard against guaranteed time bias, predictions utilizing information obtained after the progression event (n=42, 1%) were excluded. F) Bar plots demonstrates the C-Statistic and 95% C.I. for predicting OS at various time-points using the CLL-IPI, all pretreatment risk factors, interim MRD, end of therapy MRD, and CIRI. (PFS: progression-free survival; PFS36: progression-free survival at 36 months). * indicates P

Figure 4.. CIRI applied to breast adenocarcinoma.

A) The course of a patient with breast cancer receiving neoadjuvant chemotherapy followed by surgery is shown. Pretreatment risk factors (shown in blue) are obtained directly prior to therapy. Interim risk factors (shown in orange) are obtained after surgery, but before the eventual clinical outcome (DRFS36). B) Similar to Fig 2B and 3B, the displayed plot demonstrates the calibration of predictions from CIRI-BRCA with observed patient outcomes across predictions from all time-points. C) Bar plot demonstrates the C-Statistic and 95% confidence interval for predicting DRFS at various time-points using pretreatment risk factors, residual cancer burden and CIRI. Predictions from CIRI-BRCA are made after integration of all data. D) A Kaplan-Meier estimate shows the DRFS for patients stratified by CIRI-BRCA risk-prediction of DRFS36 split into three groups at the time of final restaging after integration of all information (i.e., inclusive of pathologic response). E) Similar to panel D, a Kaplan-Meier estimate shows the DRFS for patients stratified by CIRI-BRCA. Here, risk-predictions across all time-points are shown; split into 3-strata. (DRFS: distant relapse-free survival; DRFS36: distant relapse free survival at 36 months). * indicates P

Figure 5.. Robustness of CIRI and comparisons with proportional hazard modeling.

A–C) These plots demonstrate the effect of increasing levels of correlation of individual component risk predictors on CIRI in a simulated dataset. These include the effect of increasing correlation on discrimination of outcomes by C-Statistic (Panel A), calibration intercept (Panel B), and calibration slope (Panel C). D–I) To compare CIRI with Cox proportional hazard models, we trained Cox proportional hazard models starting from 20 to 200 cases drawn randomly from our validation set in CLL (D–F) and breast cancer (G–I). We then evaluated this model in independent cases (CLL, n=400; breast cancer n=150) drawn randomly from our validation set. The performance of the resulting model in this independent patient sets was compared to the performance of CIRI. Panels D–F demonstrate model performance in CLL. Panel D demonstrates the predictive performance of each model (i.e., C-Statistic) as a function of the number of training cases. Panel E demonstrates the calibration-in-the-large, or calibration intercept, of each model as a function of the number of training cases. Panel F demonstrates the calibration slope of each model as a function of the number of training cases. A perfect model has a calibration slope of 1. The confidence envelope (80%) of each statistic is shown as a shaded area, based on 250 samplings. Panels G–I are as per panels D–F, demonstrating performance in breast cancer.

Figure 6.. Prediction of therapeutic benefit in subsets of patients.

A) A schema for using interim MRD to guide therapy in CLL. Patients receive a period of induction therapy; after which interim MRD is assessed. The effect of different types of therapy can then be assessed in patients with interim MRD negative or positive disease. Here, we assessed this paradigm using patients from the CLL8, CLL10, and CLL11 clinical trials receiving chemo-immunotherapy (i.e., FCR, BR, R-chlorambucil, G-chlorambucil), blinding ourselves to the choice of therapy over the first 3 cycles. B–C) Kaplan-Meier estimates show the benefit of therapy with FCR vs alternative therapies for progression-free survival in interim MRD negative patients (Panel B) and interim MRD positive patients (Panel C). Survival is landmarked from the time of interim MRD assessment. D) A schema for using CIRI-CLL to discover predictive biomarkers to guide therapy. Patients receive a pretreatment risk-prediction (using the CLL-IPI), and then receive a period of induction therapy. At this point, interim MRD is assessed, allowing quantitative integration with CIRI. E–F) Kaplan-Meier estimates show the PFS of patients receiving FCR vs alternative therapies in patients with CIRI risk 20% (Panel F). See also Figures S5–6.