bio-longitudinal-monitoring

What this skill does

## Version Compatibility

Reference examples tested with: numpy 1.26+, pandas 2.2+, scipy 1.12+, matplotlib 3.8+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

Notes specific to this skill: this skill is statistical, not tool-bound. The hard parts are interpretive (left-censoring, multiple testing, lead-time bias), not API calls. `scipy.stats.linregress` returns a named tuple whose `.slope`/`.pvalue` attributes are stable across recent versions; the censoring-aware fit below uses only `linregress` on the uncensored decay phase plus a manual interval check, so version drift is low-risk.

# Longitudinal Monitoring

**"Track ctDNA over this course of treatment"** -> Integrate serial plasma measurements into a binary detected/not-detected trajectory plus censoring-aware burden kinetics for MRD and response monitoring.
- Python: `pandas` for the per-timepoint table, `scipy.stats` for censoring-aware decay/trend, `matplotlib` for log-scale trajectory plots

## The Single Most Important Modern Insight -- MRD is a binary integrated detection call, and "undetectable" is left-censored, not zero

A tumor-informed MRD assay does not ask "is the VAF at locus X above a threshold?" It integrates signal across the patient's entire personal variant set (16 to 500+ loci) into ONE detected/not-detected call with a defined LoD95 (the tumor fraction detected 95% of the time at a given input) and a per-sample specificity. Signal invisible at any single 0.001%-VAF locus becomes significant when summed across hundreds of loci against a modeled error background; this is why bespoke assays reach 10^-4 to 10^-6 tumor fraction. The detected/not-detected call is the unit of analysis -- per-locus VAF is plumbing, not the readout. Re-deriving a per-timepoint "VAF < X" cutoff throws away the multi-locus integration that makes MRD work and inflates false positives from a single noisy locus.

The second half of the insight: an "undetectable" result is conditional on how many genome-equivalents were interrogated. VAF=0 is LEFT-CENSORED at the per-sample LoD, not a true zero. A 10 mL tube yields roughly 50 ng cfDNA, around 15,000 haploid genome-equivalents; at 0.01% tumor fraction that is roughly 1.5 expected tumor molecules, squarely in the Poisson-limited regime (lambda < 3) where detection is stochastic. "Undetectable" at a low-input draw may simply mean the assay could not have seen the burden it saw at a higher-input draw. Every undetectable must carry its per-sample LoD; plugging 0 into a log-fit or fold-change biases everything and log(0) breaks the fit outright.

## Design Decision: tumor-informed vs tumor-naive, landmark vs surveillance

| Axis | Tumor-informed bespoke | Tumor-naive (fixed panel) |
|------|------------------------|---------------------------|
| Variant set | Patient-specific, designed from tumor/normal WES/WGS | Fixed gene panel, identical across patients |
| Examples | Signatera (16 SNVs, Reinert 2019), RaDaR (up to ~48 amplicons), INVAR (hundreds-thousands of loci, Wan 2020) | Broad cfDNA panels, sWGS |
| MRD sensitivity | Very high (10^-4 to 10^-6 TF); LoD scales with #loci x input | Lower for MRD; few loci per region |
| Needs tumor tissue | Yes (design step, weeks of turnaround) | No (tissue-free, faster) |
| CHIP confounding | Low (tracks known tumor somatic variants) | High (de novo calls include clonal hematopoiesis) |
| Best use | Defined-burden MRD/surveillance after curative intent | No tissue available, or broad genotyping in metastatic disease |

| Scenario | Recommended | Why |
|----------|-------------|-----|
| Post-curative-intent MRD / recurrence surveillance | Tumor-informed bespoke, binary call | Reaches ppm LoD by integrating across the personal variant set; CHIP-resistant |
| No tissue available, metastatic response monitoring | Tumor-naive panel, track aggregated burden | Tissue-free and immediate; accept higher LoD and mandatory CHIP control |
| Post-surgical landmark (single decisive timepoint) | One draw at ~2-10 weeks post-op | Avoids the surgical cfDNA surge; conventional Week 4 default |
| Serial surveillance over months-years | Trend over >=2 consecutive draws, confirm before acting | Each draw is another false-positive opportunity (multiple testing) |
| Defining "molecular response" | Use the assay's own validated cutoff; do not import "2-log" or "90%" blindly | Cutoffs are non-harmonized across assays (see below) |

Methodology evolves: verify the current best practice and the assay's validated definitions against the latest tool/vendor documentation before fixing any threshold in code.

## ctDNA Kinetics Biology -- why timing and shedding gate interpretation

ctDNA has a plasma half-life of roughly 2 h (114 min, Diehl 2008); broader literature spans ~16 min to 2.5 h. This fast turnover is the entire reason serial monitoring works: plasma concentration tracks CURRENT tumor flux, not a weeks-old average, while imaging tumor volume lags. The same fast clearance makes landmark timing fragile. Surgery dumps a transient cfDNA surge into plasma (tissue trauma, wound healing, neutrophil extracellular traps) that dilutes tumor fraction and can transiently raise total cfDNA. Drawing at post-op day 1-3 reads this surge, not residual disease; the conventional landmark window is ~2-10 weeks (Week 4 a frequent default). A clearance fit that includes a post-op surge point mis-estimates the half-life.

Shedding is not uniform. "ctDNA-negative" does NOT equal "disease-free": some early lung adenocarcinomas and indolent/low-volume tumors shed below detectable thresholds, and brain metastases behind the blood-brain barrier shed poorly into plasma (CSF is the better CNS compartment). A patient can have radiographic progression with clean plasma. Negativity has high negative predictive value for relapse in shedding tumors but is never a guarantee -- imaging stays mandatory for low/non-shedders and sanctuary sites.

## Molecular-Response Definitions -- and the non-standardization caveat

| Term | Representative operationalization | Caveat |
|------|-----------------------------------|--------|
| Molecular response (MR) | >= 90% drop (ctMoniTR-style) or >= 2-log/100x (immuno/heme heritage) from baseline | 2-log and 90% are different magnitudes; cutoff is study/assay-specific |
| Molecular complete response (mCR) | ctDNA becomes undetectable | "Undetectable" is LoD-conditional, not zero |
| ctDNA clearance | Sustained detectable -> undetectable | Depends on input/depth of the clearing draw; confirm with re-draw |
| Molecular progression / relapse | Confirmed re-detection or rise-from-nadir | Require trend over >=2 draws (multiple testing) |

These definitions are NOT harmonized. "2-log reduction," "90% reduction," and "molecular complete response" are assay- and study-dependent, not interchangeable. The Friends of Cancer Research ctMoniTR project is the field's standardization attempt (pooling ctDNA-change data across NSCLC immunotherapy studies to validate ctDNA change as an intermediate endpoint), not a settled standard. The FDA ctDNA guidance for curative-intent solid-tumor drug development was issued as a draft in May 2022 and finalized in November 2024; it endorses ctDNA for patient selection, MRD-based enrichment, and as a measure of response, but does NOT yet endorse ctDNA change as a validated surrogate endpoint for DFS/EFS/OS. Code should accept the assay's own validated cutoff rather than baking one in.

## Clinical Evidence and Lead Time

ctDNA MRD predicts relapse months before imaging across tumor types: breast median ~8 mo (Garcia-Murillas 2015), NSCLC median ~5.2 mo (Chaudhuri 2017), CRC mean ~8.7 mo (Reinert 2019); TRACERx phylogenetic ctDNA tracks clonal evoluti