A unifying functional dichotomy organises breast cancer molecular landscape, resolves PIK3CA ambiguity, and supports tiered tumour classification

Abstract Clinical interpretation of breast cancer sequencing is constrained not by a lack of data but by the absence of an organising framework that translates constellations of co-occurring mutations and copy-number alterations into tumour-level biology with prognostic and therapeutic meaning. This challenge is exemplified by PIK3CA, a clinically actionable alteration often treated as a single-label biomarker despite context-dependent associations with outcome. We analysed >5,000 breast tumours across multiple cohorts using integrated multi-omics (somatic mutations, copy-number, transcriptomic, proteomic and phosphoproteomic profiles) and quantified the directionality of downstream molecular consequences of recurrent alterations relative to TP53-associated trends to infer dominant tumour programmes. This revealed a robust functional organisation comprising (i) a canonical proliferative/replicative programme, enriched for cell-cycle, DNA replication and E2F signalling, and encompassing TP53 mutations and most recurrent CNAs, and (ii) a non-canonical signalling/cell-state programme marked by recurrent mutations including PIK3CA, CDH1, GATA3, MAP3K1 and AKT1, with opposing transcriptomic/proteomic directionality, comparatively lower proliferative output and a systematic tendency towards mutual exclusivity with TP53, consistent with alternative evolutionary routes. To operationalise these findings for clinical use, we developed T-OMICS ( T iered OMI CS C lassification S ystem), which layers complementary readouts to deliver a single interpretable tumour profile: Tier 1 provides a continuous genomic-risk backbone via a DNA-anchored prognostic RNA signature capturing canonical proliferative/replicative output; Tier 2 assigns programme identity based on the dominant genomic context; Tier 3 quantifies within-programme activity along a continuum; and Tier 4 overlays non-redundant modifier mutations that refine phenotype, vulnerabilities and resistance liabilities, supported by orthogonal proteomic/phosphoproteomic pathway signals. In ER+/HER2− disease, T-OMICS resolves the prognostic ambiguity of PIK3CA by showing that “PIK3CA-mutant” is not a single biological entity: in a predominant low-genomic-score context, PIK3CA aligns with buffered luminal biology and favourable outcomes, whereas in high-score contexts—conditioned by TP53 background and modifier events—PIK3CA can mark adverse biology with distinct dependencies not captured by proliferation-centric readouts; notably, low-score PIK3CA tumours with CDH1 co-mutation shift to significantly worse outcomes. Together, these results establish a programme- and state-aware framework that converts sequencing reports into clinically legible tumour biology to support risk calibration, therapeutic prioritisation and evolution-aware sampling decisions from early-stage through metastatic ER+/HER2− breast cancer. Lay Summary Breast cancer tumours often carry many genetic changes at the same time. While modern sequencing can identify these changes in detail, the results are frequently presented as long lists of mutations and DNA alterations that are difficult to interpret in terms of how a tumour behaves or how it should be treated. A well-known example is the PIK3CA gene: although it can be targeted with specific drugs, studies have reported mixed results on whether PIK3CA mutations are associated with better or worse outcomes, making it challenging to use this information confidently in clinical care. To address this problem, we analysed genomic (DNA-wide), RNA, and protein data from more than 5,000 breast tumours. We found that many common genomic changes cluster into two main biological “programmes” that reflect distinct ways tumours grow and survive. One programme is driven by rapid cell division and DNA replication and includes TP53 mutations and many common DNA copy-number changes; tumours following this programme tend to be more aggressive. The second programme is less focused on rapid growth and is defined by mutations such as PIK3CA, CDH1, GATA3, MAP3K1, and AKT1, which influence signalling and cell identity rather than directly accelerating proliferation. These programmes reflect broader tumour behaviours rather than the effects of single genes. Importantly, mutations in the second programme are usually not found alongside TP53 mutations, suggesting that breast cancers can develop through distinct biological routes—with some tumours following an alternative pathway (not overtly proliferation-dependent) that shapes their behaviour and may influence which treatments are most appropriate. Based on these findings, we developed a practical classification system, T-OMICS, for ER-positive, HER2-negative breast cancer. T-OMICS summarises which biological programme a tumour follows, how active or aggressive it is within that programme, and whether additional mutations are present that may influence treatment response or resistance. Using this framework, we show that PIK3CA mutations most often occur in a biologically buffered context associated with more favourable outcomes, but when they occur in more aggressive tumours—shaped by other key genetic changes—they can signal a higher-risk disease with different treatment needs. These findings indicate that treatment decisions should be based on the tumour’s overall biological pattern, not just the presence of a single mutation. By placing sequencing results in this broader context, T-OMICS supports more accurate risk assessment, better treatment planning, and more informed decisions about when to intensify therapy, from early-stage through advanced breast cancer. Abstract Figure Graphical Summary