Analyze serial liquid biopsy measurements to track ctDNA, tumor-derived DNA in blood. Summarize tumor fraction trends, mutation clearance, and rises after the lowest measurement through plots and structured reports using specified response thresholds.
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Tracks ctDNA dynamics over time for treatment response monitoring using serial liquid biopsy samples. Analyzes tumor fraction trends, mutation clearance kinetics, and defines molecular response criteria. Use when monitoring patients during therapy or detecting molecular relapse before clinical progression.
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Reference examples tested with: matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scipy 1.12+
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pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Track ctDNA levels over my patient's treatment" → Monitor tumor fraction and mutation dynamics across serial liquid biopsy timepoints for treatment response assessment and early relapse detection.
pandas + matplotlib for trend analysis and molecular response classificationTrack ctDNA dynamics over treatment for response assessment and relapse detection.
| Metric | Description | Clinical Relevance |
|---|---|---|
| Tumor fraction trend | Change over time | Response/progression |
| Mutation clearance | Time to undetectable | Depth of response |
| Molecular relapse | ctDNA rise | Early relapse detection |
| Lead time | ctDNA vs imaging | Months before clinical |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def analyze_tf_dynamics(patient_data):
'''
Analyze tumor fraction dynamics over treatment.
Args:
patient_data: DataFrame with columns [sample_id, timepoint, tumor_fraction, treatment_phase]
'''
# Sort by timepoint
patient_data = patient_data.sort_values('timepoint')
# Calculate log2 fold changes
baseline_tf = patient_data.iloc[0]['tumor_fraction']
patient_data['log2_fc'] = np.log2(patient_data['tumor_fraction'] / baseline_tf)
# Calculate response metrics
min_tf = patient_data['tumor_fraction'].min()
min_timepoint = patient_data.loc[patient_data['tumor_fraction'].idxmin(), 'timepoint']
metrics = {
'baseline_tf': baseline_tf,
'nadir_tf': min_tf,
'nadir_timepoint': min_timepoint,
'max_reduction': baseline_tf - min_tf,
'log2_max_reduction': np.log2(baseline_tf / min_tf) if min_tf > 0 else np.inf
}
return patient_data, metrics
def define_response(tf_series, baseline, criteria='2log'):
'''
Define molecular response based on tumor fraction changes.
Args:
tf_series: Series of tumor fractions
baseline: Baseline tumor fraction
criteria: Response criteria (e.g., '2log' for 2-log reduction)
'''
if criteria == '2log':
# 2-log (100-fold) reduction
threshold = baseline / 100
elif criteria == '1log':
threshold = baseline / 10
elif criteria == 'undetectable':
threshold = 0.001 # Assay-dependent LOD
response = tf_series < threshold
return response
def track_mutations(mutation_data):
'''
Track specific mutations across timepoints.
Args:
mutation_data: DataFrame with [timepoint, mutation, vaf]
'''
# Pivot to get VAF per mutation per timepoint
pivot = mutation_data.pivot_table(
index='timepoint',
columns='mutation',
values='vaf',
aggfunc='first'
)
# Calculate mean VAF trend
pivot['mean_vaf'] = pivot.mean(axis=1)
# Identify cleared mutations
last_timepoint = pivot.index.max()
cleared = []
for mut in pivot.columns:
if mut == 'mean_vaf':
continue
if pivot.loc[last_timepoint, mut] < 0.001 or pd.isna(pivot.loc[last_timepoint, mut]):
cleared.append(mut)
return pivot, cleared
def calculate_clearance_kinetics(mutation_data, mutation):
'''
Calculate mutation clearance half-life.
'''
mut_data = mutation_data[mutation_data['mutation'] == mutation].sort_values('timepoint')
if len(mut_data) < 3:
return None
# Log-linear regression for exponential decay
from scipy import stats
x = mut_data['timepoint'].values
y = np.log(mut_data['vaf'].values + 1e-6) # Add small value to avoid log(0)
slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)
# Half-life = ln(2) / |slope|
half_life = np.log(2) / abs(slope) if slope < 0 else np.inf
return {
'mutation': mutation,
'half_life': half_life,
'slope': slope,
'r_squared': r_value**2
}
def detect_molecular_relapse(tf_series, baseline, threshold_increase=2):
'''
Detect molecular relapse from tumor fraction series.
Args:
tf_series: DataFrame with [timepoint, tumor_fraction]
baseline: Post-treatment nadir
threshold_increase: Fold-increase to call relapse
'''
tf_series = tf_series.sort_values('timepoint')
# Find nadir
nadir_idx = tf_series['tumor_fraction'].idxmin()
nadir_tf = tf_series.loc[nadir_idx, 'tumor_fraction']
nadir_time = tf_series.loc[nadir_idx, 'timepoint']
# Check for increase after nadir
post_nadir = tf_series[tf_series['timepoint'] > nadir_time]
relapse_detected = False
relapse_timepoint = None
for idx, row in post_nadir.iterrows():
if row['tumor_fraction'] > nadir_tf * threshold_increase:
relapse_detected = True
relapse_timepoint = row['timepoint']
break
return {
'nadir_tf': nadir_tf,
'nadir_timepoint': nadir_time,
'relapse_detected': relapse_detected,
'relapse_timepoint': relapse_timepoint
}
def plot_ctdna_dynamics(patient_data, treatment_lines=None, output_file=None):
'''
Plot ctDNA dynamics over treatment.
'''
fig, ax = plt.subplots(figsize=(10, 6))
# Plot tumor fraction on log scale
ax.semilogy(patient_data['timepoint'], patient_data['tumor_fraction'],
'o-', linewidth=2, markersize=8)
# Add treatment lines
if treatment_lines:
for time, label in treatment_lines:
ax.axvline(x=time, color='gray', linestyle='--', alpha=0.5)
ax.text(time, ax.get_ylim()[1], label, rotation=90, va='top')
ax.set_xlabel('Time (days)')
ax.set_ylabel('Tumor Fraction')
ax.set_title('ctDNA Dynamics During Treatment')
# Add LOD line
ax.axhline(y=0.01, color='red', linestyle=':', alpha=0.5, label='LOD')
ax.legend()
if output_file:
plt.savefig(output_file, dpi=150, bbox_inches='tight')
return fig, ax
Goal: Generate a structured monitoring report combining tumor fraction dynamics, mutation clearance status, and molecular response classification for clinical decision support.
Approach: Aggregate tumor fraction trend analysis, mutation tracking pivot tables, and response criteria into a single report dictionary with standardized molecular response categories.
def generate_monitoring_report(patient_id, tf_data, mutation_data, imaging_data=None):
'''
Generate clinical monitoring report.
'''
report = {
'patient_id': patient_id,
'analysis_date': pd.Timestamp.now()
}
# Tumor fraction analysis
tf_data, tf_metrics = analyze_tf_dynamics(tf_data)
report['tumor_fraction'] = tf_metrics
# Mutation analysis
mutation_pivot, cleared = track_mutations(mutation_data)
report['mutations_tracked'] = len(mutation_pivot.columns) - 1
report['mutations_cleared'] = len(cleared)
# Response assessment
current_tf = tf_data.iloc[-1]['tumor_fraction']
baseline_tf = tf_data.iloc[0]['tumor_fraction']
if current_tf < 0.001:
report['response'] = 'Complete molecular response'
elif current_tf < baseline_tf * 0.01:
report['response'] = 'Major molecular response (>2 log)'
elif current_tf < baseline_tf * 0.5:
report['response'] = 'Partial molecular response'
else:
report['response'] = 'Stable/Progressive'
return report
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