Precision medicine-based platform to guide the treatment of EML4-ALK driven lung cancers and other NSCLC in real time.
Bibliographic record
Abstract
215 Background: Lung cancer (LC) is the top cause of cancer-associated mortality worldwide, with a 10-year overall survival rate of 5%. Although most LCs are smoking related, in the US, 25% of non-small cell LC (NSCLC) are diagnosed in persons with little or no smoking history. Fusions involving anaplastic lymphoma kinase (ALK) are the oncogenic driver in ~3–7% of NSCLC. While inhibitors targeting the kinase domain of ALK (TKIs) have proven extremely effective, inevitably, resistance develops with limited treatment options beyond second line TKIs. Additionally, NSCLCs without identified molecular alterations have even more limited tumor specific treatment options. Methods: We developed a precision medicine-based platform (PMP) to screen patient-derived, minimally cultured, organoid material (PDM) directly from resections with curated panels of drugs. PDM collected during clinically indicated procedures is plated in 3D-culture to generate patient-derived organoids (PDOs) and screened with drugs tailored to each tumor type. PDOs are screened at therapeutically relevant doses, drawing from pharmacokinetic data for each drug. We have optimized an assay to rapidly screen for EML4-ALK fusions and can perform next-generation sequencing in real time (~7 days) to integrate with drug screening results. Our organoid cultures retain the full spectrum of tumor microenvironment present in the original sample. Results: To date, we have analyzed 83 cases, including 8 EML4-ALK NSCLC. We have demonstrated the ability to produce high quality data from low input samples (biopsies). In one EML4-ALK NSCLC we were able to collect PDM from two distinct anatomic spaces (pleural effusion and peritoneal fluid) and screen with the same panel of drugs, with nearly identical results, highlighting the reproducibility and consistency of our assay. Screening of EML4-ALK tumors which have progressed to second or higher line TKIs, demonstrate sensitivity to earlier generation ALK TKIs, a known phenomenon. Characterization of tumors with unknown clinical drivers identifies that ~1/3 tumors which have no actionable or hypothetically prioritized variants, and they exhibit particularly poor response to chemotherapies. Our results recapitulate known resistance/progression in samples previously exposed to therapy, demonstrating a strong negative predictive value. Longitudinal assessment is being tracked to robustly assess positive predictive value (PPV). Conclusions: Our PMP captures robust and reproducible results that are consistent with known clinical pathogenesis. Moving forward, we are collecting longitudinal data from enrolled patients in parallel with clinical trials to demonstrate the PPV of our PMP. We additionally strive to demonstrate reproducibility to obtain Clinical Laboratory Improvement Amendments approval and to deliver results to patients and physicians to help guide clinical care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".