MrdLongitudinalTrendChecker

MRD Longitudinal Trend

Liquid Biopsy жидкостная биопсия cfDNA ctDNA CTC exosomes NGS qPCR

Utility description: MRD Longitudinal Trend

MRD Longitudinal Trend Checker — Specialized Minimal Residual Disease Dynamics Analysis

Performs MRD-specific longitudinal analysis per ESMO/ASCO MRD guidelines, TRACERx and FDA surrogate endpoint standards. Classifies trends as SUSTAINED_MRD_NEGATIVE / CLEARANCE_ACHIEVED / MOLECULAR_RELAPSE / PERSISTENT_MRD_POSITIVE / INDETERMINATE. Handles censored data (undetected ≠ zero), detects rising trends from undetectable state, counts consecutive negatives for clearance confirmation, and correlates with imaging (concordant/discordant assessment). Molecular relapse typically precedes imaging by 2-6 months. Single detection after negatives requires confirmation before clinical action. Persistent MRD+ post-curative treatment indicates residual disease and adjuvant therapy candidacy.

⚠️ Critical: MRD monitoring operates at ASSAY SENSITIVITY LIMIT. Molecular relapse precedes radiological relapse. Clearance requires ≥2 consecutive negatives. Decisions must be made by MULTIDISCIPLINARY TEAM. MRD is prognostic biomarker, not diagnostic test.

input.csv

PatientID,AssayName,ClinicalContext,TimepointData,AssayLOD_Percent,AssayLOQ_Percent,TreatmentEndDate,LastImagingDate,LastImagingResult,MinTimepointsForTrend,RisingTrend_FoldChange_Threshold,ConsecutiveNegative_ForClearance
MRD-PAT-001,Signatera_NSCLC,Post_Curative_Treatment,"2026-01-15:0.008:true;2026-02-20:0.002:true;2026-03-25:0:false;2026-05-01:0:false;2026-06-10:0:false",0.001,0.005,2026-01-10,2026-06-05,NED,3,2.0,2
MRD-PAT-002,Signatera_CRC,Adjuvant_Monitoring,"2026-01-10:0:false;2026-02-15:0:false;2026-03-20:0.003:true;2026-04-25:0.012:true;2026-06-01:0.045:true",0.001,0.005,2025-12-20,2026-05-28,NED,3,2.0,2
MRD-PAT-003,RaDaR_Breast,Post_Curative_Treatment,"2026-02-01:0.015:true;2026-03-10:0.010:true;2026-04-15:0.008:true;2026-05-20:0.012:true",0.001,0.005,2026-01-25,2026-05-15,NED,3,2.0,2
MRD-PAT-004,Signatera_NSCLC,Adjuvant_Monitoring,"2026-05-01:0:false;2026-06-10:0:false",0.001,0.005,2026-04-15,2026-06-05,NED,3,2.0,2

URS & FS — User Requirements and Functional Specification

This document describes the controlled interface and behaviour of MrdLongitudinalTrendChecker for MRD Longitudinal Trend Checker.

Domain limits URS input.csv FS IQ/OQ/PQ

Domain limits and critical parameters

Key fragments from the source description are shown below. Before production use, limits must be verified against the approved specification, registration dossier and local SOPs.

⚠️ Critical: MRD monitoring operates at ASSAY SENSITIVITY LIMIT. Molecular relapse precedes radiological relapse. Clearance requires ≥2 consecutive negatives. Decisions must be made by MULTIDISCIPLINARY TEAM. MRD is prognostic biomarker, not diagnostic test.

URS — User Requirements Specification

ID	Requirement	Criticality	Acceptance criterion
URS-001	The utility shall accept an input.csv file for MRD Longitudinal Trend Checker with headers defined in the data contract.	High	The file is processed without manual header editing.
URS-002	The utility shall perform deterministic QC evaluation without machine learning and without probabilistic conformance decisions.	High	Identical input data, rule version and configuration produce reproducible results.
URS-003	The utility shall validate mandatory fields, data types, ranges, units and domain plausibility.	High	Schema, conversion and range errors are explicitly reported.
URS-004	The utility shall apply domain limits and rules from the description, approved specification, registration dossier and local SOPs.	High	Each check has PASS/WARNING/FAIL and a clear message.
URS-005	The utility shall generate output.json with machine-readable results, source values, warnings, failures and critical findings.	High	JSON is suitable for LIMS/ELN/MES integration and QA/QC review.
URS-006	The utility shall preserve traceability between batch/sample, input file, applied rules and final status.	High	Output contains identifiers, checked parameters and audit metadata.
URS-007	The documentation shall support IQ/OQ/PQ, CSV/CSA and review by internal QA or inspectors.	Medium	URS, FS, input/output contract and test scenarios are supplied with the utility.
URS-008	The utility shall be used as a QC decision-support tool and not as a substitute for approved specifications and QA/QP release decision.	Medium	Documentation states change control and limit-verification expectations.

input.csv contract

#	Field	Type	Sample	Purpose
1	`PatientID`	string / controlled vocabulary	`MRD-PAT-001`	Controlled input parameter for deterministic QC rules.
2	`AssayName`	string / controlled vocabulary	`Signatera_NSCLC`	Controlled input parameter for deterministic QC rules.
3	`ClinicalContext`	string / controlled vocabulary	`Post_Curative_Treatment`	Controlled input parameter for deterministic QC rules.
4	`TimepointData`	string / controlled vocabulary	`2026-01-15:0.008:true;2026-02-20:0.002:true;2026-03-25:0:false;2026-05-01:0:false;2026-06-10:0:false`	Time parameter for process, incubation, storage or analytical workflow.
5	`AssayLOD_Percent`	decimal	`0.001`	Controlled input parameter for deterministic QC rules.
6	`AssayLOQ_Percent`	decimal	`0.005`	Controlled input parameter for deterministic QC rules.
7	`TreatmentEndDate`	string / controlled vocabulary	`2026-01-10`	Controlled input parameter for deterministic QC rules.
8	`LastImagingDate`	string / controlled vocabulary	`2026-06-05`	Controlled input parameter for deterministic QC rules.
9	`LastImagingResult`	string / controlled vocabulary	`NED`	Result/status used in final classification.
10	`MinTimepointsForTrend`	decimal	`3`	Time parameter for process, incubation, storage or analytical workflow.
11	`RisingTrend_FoldChange_Threshold`	decimal	`2.0`	Controlled input parameter for deterministic QC rules.
12	`ConsecutiveNegative_ForClearance`	decimal	`2`	Controlled input parameter for deterministic QC rules.

PatientID,AssayName,ClinicalContext,TimepointData,AssayLOD_Percent,AssayLOQ_Percent,TreatmentEndDate,LastImagingDate,LastImagingResult,MinTimepointsForTrend,RisingTrend_FoldChange_Threshold,ConsecutiveNegative_ForClearance
MRD-PAT-001,Signatera_NSCLC,Post_Curative_Treatment,2026-01-15:0.008:true;2026-02-20:0.002:true;2026-03-25:0:false;2026-05-01:0:false;2026-06-10:0:false,0.001,0.005,2026-01-10,2026-06-05,NED,3,2.0,2
MRD-PAT-002,Signatera_CRC,Adjuvant_Monitoring,2026-01-10:0:false;2026-02-15:0:false;2026-03-20:0.003:true;2026-04-25:0.012:true;2026-06-01:0.045:true,0.001,0.005,2025-12-20,2026-05-28,NED,3,2.0,2
MRD-PAT-003,RaDaR_Breast,Post_Curative_Treatment,2026-02-01:0.015:true;2026-03-10:0.010:true;2026-04-15:0.008:true;2026-05-20:0.012:true,0.001,0.005,2026-01-25,2026-05-15,NED,3,2.0,2

Input validation rules

ID	Field	Rule	Criticality
VR-001	`PatientID`	The field shall match an approved dictionary or accepted string representation.	High
VR-002	`AssayName`	The field shall match an approved dictionary or accepted string representation.	High
VR-003	`ClinicalContext`	The field shall match an approved dictionary or accepted string representation.	High
VR-004	`TimepointData`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-005	`AssayLOD_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-006	`AssayLOQ_Percent`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-007	`TreatmentEndDate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-008	`LastImagingDate`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-009	`LastImagingResult`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-010	`MinTimepointsForTrend`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-011	`RisingTrend_FoldChange_Threshold`	The field shall match an approved dictionary or accepted string representation.	Medium
VR-012	`ConsecutiveNegative_ForClearance`	The field shall match an approved dictionary or accepted string representation.	Medium

FS — Functional Specification

ID	Function	Implementation
FS-001	CLI execution	Support execution modes: demo mode without arguments and production mode input.csv output.json.
FS-002	CSV import	Read input.csv in UTF-8/CSV-compatible format and validate header and expected columns.
FS-003	Schema validation	Check mandatory fields, column count, unknown key fields and empty mandatory values.
FS-004	Type conversion	Convert numeric, flag and text values; invalid format is recorded as a row-level error.
FS-005	Domain rule engine	Apply rules for MRD Longitudinal Trend Checker, including critical limits from the description and approved specification.
FS-006	Status aggregation	Produce final status: FAIL for critical failure, WARNING for non-critical deviation, PASS for conformance.
FS-007	JSON export	Write output.json with detailed checks, source values, warnings, failures and critical findings.
FS-008	Audit support	Keep result structure suitable for review, deviation investigation and calculation reproduction.
FS-009	Integration contract	Support the scenario LIMS/ELN/MES → input.csv → utility → output.json → portal/admin review.
FS-010	Error handling	Return explicit messages for missing file, empty CSV, invalid schema, output write failure and invalid format.

Example output.json

{
  "utilityId": "mrdlongitudinaltrendchecker",
  "utilityFolder": "MrdLongitudinalTrendChecker",
  "package": "LiquidBiopsy",
  "overallStatus": "PASS|WARNING|FAIL",
  "sourceFile": "input.csv",
  "processedAtUtc": "2026-06-10T00:00:00Z",
  "checks": [
    {
      "parameter": "PatientID",
      "value": "MRD-PAT-001",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-001"
    },
    {
      "parameter": "AssayName",
      "value": "Signatera_NSCLC",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-002"
    },
    {
      "parameter": "ClinicalContext",
      "value": "Post_Curative_Treatment",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-003"
    },
    {
      "parameter": "TimepointData",
      "value": "2026-01-15:0.008:true;2026-02-20:0.002:true;2026-03-25:0:false;2026-05-01:0:false;2026-06-10:0:false",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-004"
    },
    {
      "parameter": "AssayLOD_Percent",
      "value": "0.001",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-005"
    },
    {
      "parameter": "AssayLOQ_Percent",
      "value": "0.005",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-006"
    },
    {
      "parameter": "TreatmentEndDate",
      "value": "2026-01-10",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-007"
    },
    {
      "parameter": "LastImagingDate",
      "value": "2026-06-05",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-008"
    },
    {
      "parameter": "LastImagingResult",
      "value": "NED",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-009"
    },
    {
      "parameter": "MinTimepointsForTrend",
      "value": "3",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-010"
    },
    {
      "parameter": "RisingTrend_FoldChange_Threshold",
      "value": "2.0",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-011"
    },
    {
      "parameter": "ConsecutiveNegative_ForClearance",
      "value": "2",
      "status": "PASS|WARNING|FAIL",
      "message": "Deterministic rule-based check result",
      "ruleReference": "FS-RULE-012"
    }
  ],
  "criticalFindings": [],
  "warnings": [],
  "audit": {
    "inputHash": "sha256:<calculated at runtime>",
    "rulesVersion": "<utility executable version>",
    "documentation": "MrdLongitudinalTrendChecker.documentation.html"
  }
}

Traceability matrix

URS	FS	Test	Evidence
URS-001	FS-001, FS-002	OQ-001	Verify execution and import of valid input.csv.
URS-002	FS-005, FS-006	OQ-004	Repeat the same dataset and compare output.json.
URS-003	FS-003, FS-004, FS-010	OQ-002, OQ-003	Verify missing columns and invalid types.
URS-004	FS-005, FS-006	OQ-004, PQ-001	Verify critical deviations on real/boundary data.
URS-005	FS-007, FS-009	OQ-005	Verify JSON schema and downstream-system suitability.
URS-006	FS-008	OQ-006	Verify identifiers and audit metadata.
URS-007	FS-008, FS-010	IQ-001, OQ-007	Verify documentation completeness and control evidence.
URS-008	FS-005, FS-008	PQ-002	Verify review workflow and no replacement of QA decision.

IQ/OQ/PQ test scenarios

ID	Scenario	Expected result
IQ-001	Verify executable, input.csv, documentation and checksum availability.	Delivery set is complete; version is recorded.
OQ-001	Valid sample row from input.csv.	PASS or acceptable WARNING according to rules.
OQ-002	Remove a mandatory CSV column.	Schema error or FAIL with missing-column reference.
OQ-003	Place a non-numeric value into a numeric field.	Type-conversion error with row/field reference.
OQ-004	Set a critical parameter outside the limit.	FAIL and critical finding.
OQ-005	Verify output.json structure.	All mandatory sections are present and JSON is valid.
OQ-006	Verify batch/sample traceability.	Input and result identifiers match.
PQ-001	Verify 3–5 real user batches/samples.	Result is confirmed by QC/QA review.
PQ-002	Verify deviation workflow and manual QA decision.	Utility supports review but does not replace approved decision.

QA/QC and change control

Do not rename columns without updating validator, documentation and test set.
Retain input.csv, output.json, executable version and checksum.
Before production use, perform IQ/OQ/PQ or equivalent CSV/CSA verification.
Critical limits shall be verified against the approved specification, registration dossier and local SOPs.
The utility provides structured QC decision support; final release decision remains with QA/QP and approved procedures.

Included in packages

Liquid Biopsy QC Suite

QC and pre-analytical control package for liquid biopsy workflows: cfDNA/ctDNA, CTC, EV/exosomes, methylation, NGS/qPCR/ddPCR, sample quality, contamination, sensitivity and reporting checks.

Open