Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (Nature Medicine, (2022), 28, 5, (924-933), 10.1038/s41591-022-01772-9)

In the version of this article initially published, a list of the DECIDE-AI expert group members and their affiliations was omitted and has now been included in the HTML and PDF versions of the article. *A list of authors and their affiliations appears online

HER2-family signalling mechanisms, clinical implications and targeting in breast cancer.

Approximately 20 % of human breast cancers (BC) overexpress HER2 protein, and HER2-positivity is associated with a worse prognosis. Although HER2-targeted therapies have significantly improved outcomes for HER2-positive BC patients, resistance to trastuzumab-based therapy remains a clinical problem. In order to better understand resistance to HER2-targeted therapies in HER2-positive BC, it is necessary to examine HER family signalling as a whole. An extensive literature search was carried out to critically assess the current knowledge of HER family signalling in HER2-positive BC and response to HER2-targeted therapy. Known mechanisms of trastuzumab resistance include reduced receptor-antibody binding (MUC4, p95HER2), increased signalling through alternative HER family receptor tyrosine kinases (RTK), altered intracellular signalling involving loss of PTEN, reduced p27kip1, or increased PI3K/AKT activity and altered signalling via non-HER family RTKs such as IGF1R. Emerging strategies to circumvent resistance to HER2-targeted therapies in HER2-positive BC include co-targeting HER2/PI3K, pan-HER family inhibition, and novel therapies such as T-DM1. There is evidence that immunity plays a key role in the efficacy of HER-targeted therapy, and efforts are being made to exploit the immune system in order to improve the efficacy of current anti-HER therapies. With our rapidly expanding understanding of HER2 signalling mechanisms along with the repertoire of HER family and other targeted therapies, it is likely that the near future holds further dramatic improvements to the prognosis of women with HER2-positive BC

Deregulation of the EGFR/PI3K/PTEN/Akt/mTORC1 pathway in breast cancer: possibilities for therapeutic intervention

The EGFR/PI3K/PTEN/Akt/mTORC1/GSK-3 pathway plays prominent roles in malignant transformation, prevention of apoptosis, drug resistance and metastasis. The expression of this pathway is frequently altered in breast cancer due to mutations at or aberrant expression of: HER2, ERalpha, BRCA1, BRCA2, EGFR1, PIK3CA, PTEN, TP53, RB as well as other oncogenes and tumor suppressor genes. In some breast cancer cases, mutations at certain components of this pathway (e.g., PIK3CA) are associated with a better prognosis than breast cancers lacking these mutations. The expression of this pathway and upstream HER2 has been associated with breast cancer initiating cells (CICs) and in some cases resistance to treatment. The anti-diabetes drug metformin can suppress the growth of breast CICs and herceptin-resistant HER2+ cells. This review will discuss the importance of the EGFR/PI3K/PTEN/Akt/mTORC1/GSK-3 pathway primarily in breast cancer but will also include relevant examples from other cancer types. The targeting of this pathway will be discussed as well as clinical trials with novel small molecule inhibitors. The targeting of the hormone receptor, HER2 and EGFR1 in breast cancer will be reviewed in association with suppression of the EGFR/PI3K/PTEN/Akt/mTORC1/GSK-3 pathway.USAMRMC {[}BC022276]; Intramural RECDA Award; Italian Association for Cancer Research (AIRC); MIUR-PRIN; Italian MIUR-FIRB Accordi di Programma; Italian ``Ministero dell'Istruzione, dell'Universita e della Ricerca (Ministry for Education, Universities and Research) - FIRB-MERIT {[}RBNE08YYBM]; Italian Ministry of Economy and Finance; Italian Ministry of Health, Ricerca Finalizzata Stemness; MIUR FIRB {[}RBAP11ZJFA\_001]; CRO; Italian Association for Cancer Research, (AIRC) (RM PI); Italian Association for Cancer Research, (AIRC) {[}MCO10016]; Italian Ministry of Health; Regione Friuli Venezia-Giuli

Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials

Prostate cancer is the most frequent cancer in men and a leading cause of cancer death. Determining a patient\u27s optimal therapy is a challenge, where oncologists must select a therapy with the highest likelihood of success and the lowest likelihood of toxicity. International standards for prognostication rely on non-specific and semi-quantitative tools, commonly leading to over- and under-treatment. Tissue-based molecular biomarkers have attempted to address this, but most have limited validation in prospective randomized trials and expensive processing costs, posing substantial barriers to widespread adoption. There remains a significant need for accurate and scalable tools to support therapy personalization. Here we demonstrate prostate cancer therapy personalization by predicting long-term, clinically relevant outcomes using a multimodal deep learning architecture and train models using clinical data and digital histopathology from prostate biopsies. We train and validate models using five phase III randomized trials conducted across hundreds of clinical centers. Histopathological data was available for 5654 of 7764 randomized patients (71%) with a median follow-up of 11.4 years. Compared to the most common risk-stratification tool-risk groups developed by the National Cancer Center Network (NCCN)-our models have superior discriminatory performance across all endpoints, ranging from 9.2% to 14.6% relative improvement in a held-out validation set. This artificial intelligence-based tool improves prognostication over standard tools and allows oncologists to computationally predict the likeliest outcomes of specific patients to determine optimal treatment. Outfitted with digital scanners and internet access, any clinic could offer such capabilities, enabling global access to therapy personalization