bioRxiv Science⌕ Search

Biology subjects

Guerini-Rocco, E.

Publications and source records attributed to Guerini-Rocco, E..

2 recordsLinked to original sources

Aberrant subcellular localization of mismatch repair protein MLH1 dysregulates the cell cycle to create new therapeutic opportunities

Estrogen receptor positive (ER+) breast cancer is one of the most common causes of cancer-related death in women. Mortality is largely driven by recurrence of treatment-resistant disease after many years of apparent response, making the molecular events that cause recurrence a critical area of investigation. Loss of expression of MLH1, a tumor suppressor best studied in its role in mismatch repair, induces resistance of ER+ breast cancer cells to standard estrogen-targeting therapies. It does so by delinking cell cycle progression from estrogen regulation, a role distinct from its function in mismatch repair. MLH1 loss, as currently clinically diagnosed by detecting genomic instability or by immunohistochemistry for absence of protein, occurs in 12-15% of all cancers. Here, we demonstrate that sub-clonal, patient-derived mutations in MLH1, which neither impact protein abundance nor contribute sufficiently to genomic instability to be detected diagnostically, seed endocrine treatment resistance by enabling estrogen-independent growth in vitro, ex vivo in patient-derived organoids (p=0.005) and in vivo (p=0.0001). The mechanism underlying this endocrine treatment resistance is aberrant localization of MLH1 to the cytoplasm in vitro and in vivo (p=0.04), which precludes cell cycle arrest in response to endocrine therapy while simultaneously rendering cells acutely dependent on CDK4/6 activity. Consequently, administration of CDK4/6 inhibitors causes extreme regression in cells with cytoplasmic MLH1 compared to control cell populations with nuclear localization of MLH1 in vitro (p=0.00000009), ex vivo (p=0.01) and in vivo (p=0.01). As aberrant cytoplasmic localization occurs in an additional [~]12% of ER+ breast cancer patients, it constitutes a new, major contributor to MLH1 dysregulation. The potential applicability of cytoplasmic MLH1 as a predictor of responsiveness to existing targeted therapies in a hard-to-treat breast cancer subtype posits an update of current clinical diagnostic criteria and therapeutic strategies. This is particularly important in the adjuvant setting where identification of biomarkers predicting responsiveness to CDK4/6 inhibitors remains an urgent, unmet clinical need.

cancer biology↗

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

For patients with hormone receptor-positive, early breast cancer without HER2 amplification, multigene expression assays including Oncotype DX (R) recurrence score (RS) have been clinically validated to identify patients who stand to derive added benefit from adjuvant cytotoxic chemotherapy. However, cost and turnaround time have limited its global adoption despite recommendation by practice guidelines. We investigated if routinely available hematoxylin and eosin (H&E)-stained pathology slides could act as a surrogate triaging data substrate by predicting RS using machine learning methods. We trained and validated a multimodal transformer model, Orpheus, using 6,203 patients across three independent cohorts, taking both H&E images and their corresponding synoptic text reports as input. We showed accurate inference of recurrence score from whole-slide images (r = 0.63 (95% C.I. 0.58 - 0.68); n = 1,029), the raw text of their corresponding reports (r = 0.58 (95% C.I. 0.51 - 0.64); n = 972), and their combination (r = 0.68 (95% C.I. 0.64 - 0.73); n = 964) as measured by Pearsons correlation. To predict high-risk disease (RS>25), our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 (95% C.I. 0.83 - 0.94), and area under the precision recall curve (AUPRC) of 0.64 (95% C.I. 0.60 - 0.82), compared to 0.49 (95% C.I. 0.36 - 0.64) for an existing nomogram based on clinical and pathologic features. Moreover, our model generalizes well to external international cohorts, effectively identifying recurrence risk (r = 0.61, p < 10-4, n = 452; r = 0.60, p < 10-4, n = 575) and high-risk status (AUROC = 0.80, p < 10-4, AUPRC = 0.68, p < 10-4, n = 452; AUROC = 0.83, p < 10-4, AUPRC = 0.73, p < 10-4, n = 575) from whole-slide images. Probing the biologic underpinnings of the model decisions uncovered tumor cell size heterogeneity, immune cell infiltration, a proliferative transcription program, and stromal fraction as correlates of higher-risk predictions. We conclude that at an operating point of 94.4% precision and 33.3% recall, this model could help increase global adoption and shorten lag between resection and adjuvant therapy.

pathology↗