NSCLC splits into five biomarker-defined segments, each with its own treatment standard, and 51% of patients are diagnosed only after the disease has already reached distant stage.
Non-small cell lung cancer is not one disease to size. Adenocarcinoma accounts for roughly 40% of all lung cancers, squamous cell carcinoma for 25 to 30%, and large cell carcinoma for about 10% (American Cancer Society). Within that histology map sits the segmentation that actually drives treatment: EGFR, ALK, ROS1, KRAS G12C, and PD-L1 expression tier. Each segment carries a different first-line standard of care, and sizing the wrong one is the most common pre-launch error.
The US will see an estimated 229,410 new lung cancer diagnoses in 2026, and non-small cell histology accounts for roughly 85% of them (American Cancer Society; SEER). Fifty-one percent of cases present at distant stage against just 24% localized (SEER, 2016-2022 diagnosis data). The biomarker-driven segment map below only helps a patient whose disease is caught before it reaches that point.
Below, we segment the population the way your commercial model has to, trace where each biomarker cohort is actually identified and treated, and show where the identification gap costs patients before therapy ever starts.
Exhibit 2 — NSCLC molecular segmentation, US and North American cohorts.
| Segment | Approx. share of NSCLC | Dominant 1L approach | Identification depends on | Commercial note |
|---|---|---|---|---|
| EGFR-mutantAdenocarcinoma-skewed, never-smoker enriched | ~17–19% | EGFR TKI (osimertinib) | NGS or EGFR-specific PCR at diagnosis | Crowded first-line; LCMC (JAMA 2014) and EXPRESS (Lung Cancer 2019) bound the range |
| ALK-positiveYounger, never-smoker skew | ~3–9%, population-dependent | ALK TKI | FISH or NGS | US community-practice data (Oncotarget 2021) shows 2.6% overall, rising to 9.3% in non-smoking non-squamous patients. Alectinib is preferred over crizotinib per the ALEX trial (Peters et al., NEJM 2017, PMID 28586279). |
| KRAS G12CSmoking-associated | ~10–13% (8.9–19.5% real-world range) | Chemo-IO; sotorasib or adagrasib at progression | NGS | Lim et al. (Lung Cancer 2023) is the reference range; testing is recommended before first-line therapy |
| PD-L1 high (≥50%), driver-negativeSee CI brief | ~21–27% of driver-negative NSCLC | IO monotherapy | PD-L1 IHC (22C3 pharmDx) | EXPRESS (2019): 21% in the Americas overall, rising to 27% once EGFR/ALK-negative. IO monotherapy is the standard here per KEYNOTE-024 (Reck et al., NEJM 2016, PMID 27718847); the full competitive map is in the CI brief. |
| [YOUR SEGMENT][Client] · Confidential | Defined at intake | Scope-dependent | Per proposed biomarker | Sized to your asset |
Sources: Segment prevalence is drawn from four live-verified sources: Kris et al., Lung Cancer Mutation Consortium, JAMA 2014, PMID 24846037; Dietel et al., the EXPRESS study, Lung Cancer 2019, PMID 31319978; Allen et al., Oncotarget 2021, PMID 34786182; and Lim et al., Lung Cancer 2023, PMID 37683526. Treatment-standard citations: Peters et al. (ALEX), NEJM 2017, PMID 28586279 (alectinib vs. crizotinib, ALK-positive NSCLC); Reck et al. (KEYNOTE-024), NEJM 2016, PMID 27718847 (pembrolizumab monotherapy, PD-L1 ≥50%). On a commissioned brief, each figure is re-verified against the current published record and matched to your asset's specific proposed label and cohort.
Five questions. Each section is built to answer one of them.
Every section answers a named commercial question your team is asking, scoped to your asset.
Delivers
- Histology split · biomarker-defined segmentation · dominant 1L approach per segment
Delivers
- SEER stage-at-diagnosis distribution · localized vs. distant split · five-year survival differential by stage
Delivers
- EGFR, ALK, KRAS G12C, and PD-L1 prevalence from US and North American cohorts · testing-population caveats
Delivers
- NCCN member institutions · NCI ALCHEMIST and Lung-MAP screening networks · named coordinating sites
Delivers
- Tissue-NGS vs. liquid-biopsy pathway · turnaround times · guideline-complete testing rate before first-line therapy
Scoped to your asset's target segment — not the disease in aggregate.
Scope Your WorkWhat's inside
- Histologic subtypes: adenocarcinoma, squamous cell, large cell
- The biomarker segmentation that actually drives treatment selection
- What secondary data resolves vs. what needs primary work
- US incidence and the non-small cell share of lung cancer
- Stage-at-diagnosis distribution and the localized-to-distant gap
- Five-year survival differential by stage
- EGFR, ALK, ROS1, and KRAS G12C prevalence across US and North American cohorts
- PD-L1 expression tiers and the driver-negative population
- Where academic-center and community-practice estimates diverge
- NCCN member institutions setting thoracic oncology practice
- NCI ALCHEMIST and Lung-MAP screening and treatment networks
- Named coordinating sites and principal investigators
- Tissue biopsy with reflex NGS panel as the standard first step
- Liquid biopsy (ctDNA) as concurrent or reflex complement
- PD-L1 IHC and turnaround-time benchmarks by test
- Share of patients receiving all guideline-recommended biomarkers before first-line therapy
- Diagnosis-to-treatment timeline and where it stalls
- What faster biomarker turnaround changes for each segment
- Which population figures are solid and which are modelled
- The inputs that drive estimate variance
- Sensitivity on testing penetration
- Evidence gaps secondary research cannot close
- Where your segment definition needs primary validation
- Decisions contingent on label scope
Included with every brief
Every figure is live-sourced before delivery. If a number cannot be verified, it does not appear.
Prepared by MoatRx analysts.
This is a field where AI confidently reproduces outdated epidemiology, superseded payer policy, and retracted analyses. AXLRx uses none of its own memory as a source. Every figure your team receives is verified against a live document at the time of writing.
A wrong number in front of your payer or your leadership team is not recoverable in the same meeting.
- Every claim cited to a live PMID, ClinicalTrials.gov ID, or URL at point of writing — uncited claims are dropped, not estimated
- PubMed metadata fetched live during authoring — model memory produces incorrect author and journal data even on correct PMIDs
- Numeric cross-check: the specific figure must appear in the cited source, not merely be consistent with its topic
- Independent audit pass after generation — broken links, unsourced claims, and numeric inconsistencies flagged before delivery
- Drop gate: any figure that cannot clear the above is removed. No confidence tiers. No exceptions.
Frequently asked questions
Tell us your asset. Your team has the intelligence in 72 hours.
We build from your asset's clinical profile — mechanism, biomarker strategy, proposed label, and target cohort. Scope confirmation takes one call.
Drug, mechanism, proposed indication, target cohort, geography. Five minutes via the intake form.
We confirm scope with your team, clarify ambiguities, and lock delivery timing.
PDF intelligence document, Excel model, and optional executive deck — with a 30-minute readout call included.