Mouse models in the era of large human tumour sequencing studies

Atlas of Lobular Breast Cancer Models: Challenges and Strategic Directions

Invasive lobular carcinoma (ILC) accounts for up to 15% of all breast cancer (BC) cases and responds well to endocrine treatment when estrogen receptor α-positive (ER+) yet differs in many biological aspects from other ER+ BC subtypes. Up to 30% of patients with ILC will develop late-onset metastatic disease up to ten years after initial tumor diagnosis and may experience failure of systemic therapy. Unfortunately, preclinical models to study ILC progression and predict the efficacy of novel therapeutics are scarce. Here, we review the current advances in ILC modeling, including cell lines and organotypic models, genetically engineered mouse models, and patient-derived xenografts. We also underscore four critical challenges that can be addressed using ILC models: drug resistance, lobular tumor microenvironment, tumor dormancy, and metastasis. Finally, we highlight the advantages of shared experimental ILC resources and provide essential considerations from the perspective of the European Lobular Breast Cancer Consortium (ELBCC), which is devoted to better understanding and translating the molecular cues that underpin ILC to clinical diagnosis and intervention. This review will guide investigators who are considering the implementation of ILC models in their research programs.

Current methods in translational cancer research

Recent developments in pre-clinical screening tools, that more reliably predict the clinical effects and adverse events of candidate therapeutic agents, has ushered in a new era of drug development and screening. However, given the rapid pace with which these models have emerged, the individual merits of these translational research tools warrant careful evaluation in order to furnish clinical researchers with appropriate information to conduct pre-clinical screening in an accelerated and rational manner. This review assesses the predictive utility of both well-established and emerging pre-clinical methods in terms of their suitability as a screening platform for treatment response, ability to represent pharmacodynamic and pharmacokinetic drug properties, and lastly debates the translational limitations and benefits of these models. To this end, we will describe the current literature on cell culture, organoids, in vivo mouse models, and in silico computational approaches. Particular focus will be devoted to discussing gaps and unmet needs in the literature as well as current advancements and innovations achieved in the field, such as co-clinical trials and future avenues for refinement.

Mouse Models of Myeloid Malignancies

Mouse models of human myeloid malignancies support the detailed and focused investigation of selected driver mutations and represent powerful tools in the study of these diseases. Carefully developed murine models can closely recapitulate human myeloid malignancies in vivo, enabling the interrogation of a number of aspects of these diseases including their preclinical course, interactions with the microenvironment, effects of pharmacological agents, and the role of non-cell-autonomous factors, as well as the synergy between co-occurring mutations. Importantly, advances in gene-editing technologies, particularly CRISPR-Cas9, have opened new avenues for the development and study of genetically modified mice and also enable the direct modification of mouse and human hematopoietic cells. In this review we provide a concise overview of some of the important mouse models that have advanced our understanding of myeloid leukemogenesis with an emphasis on models relevant to clonal hematopoiesis, myelodysplastic syndromes, and acute myeloid leukemia with a normal karyotype.

Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research

Patient-derived organoids (PDO) and patient-derived xenografts (PDX) continue to emerge as important preclinical platforms for investigations into the molecular landscape of cancer. While the advantages and disadvantage of these models have been described in detail, this review focuses in particular on the bioinformatics and state-of-the art techniques that accompany preclinical model development. We discuss the strength and limitations of currently used technologies, particularly 'omics profiling and bioinformatics analyses, in addressing the 'efficacy' of preclinical models, both for tumour characterization as well as their use in identifying potential therapeutics. We select pancreatic ductal adenocarcinoma (PDAC) as a case study to highlight the state of the art of the field, and address new avenues for improved bioinformatics characterization of preclinical models.

Investigating Tumor Heterogeneity in Mouse Models

Cancer arises from a single cell through a series of acquired mutations and epigenetic alterations. Tumors gradually develop into a complex tissue comprised of phenotypically heterogeneous cancer cell populations, as well as noncancer cells that make up the tumor microenvironment. The phenotype, or state, of each cancer and stromal cell is influenced by a plethora of cell-intrinsic and cell-extrinsic factors. The diversity of these cellular states promotes tumor progression, enables metastasis, and poses a challenge for effective cancer treatments. Thus, the identification of strategies for the therapeutic manipulation of tumor heterogeneity would have significant clinical implications. A major barrier in the field is the difficulty in functionally investigating heterogeneity in tumors in cancer patients. Here we review how mouse models of human cancer can be leveraged to interrogate tumor heterogeneity and to help design better therapeutic strategies.