Targeted therapy and personalized medicine in hepatocellular carcinoma: drug resistance, mechanisms, and treatment strategies

Drug resistance remains one of the greatest challenges facing precision oncology today. Despite the vast array of resistance mechanisms that cancer cells employ to subvert the effects of targeted therapy, a deep understanding of cancer signalling networks has led to the development of novel strategies to tackle resistance both in the first-line and salvage therapy settings. In this review, we provide a brief overview of the major classes of resistance mechanisms to targeted therapy, including signalling reprogramming and tumour evolution; our discussion also focuses on the use of different forms of polytherapies (such as inhibitor combinations, multi-target kinase inhibitors and HSP90 inhibitors) as a means of combating resistance. The promise and challenges facing each of these polytherapies are elaborated with a perspective on how to effectively deploy such therapies in patients. We highlight efforts to harness computational approaches to predict effective polytherapies and the emerging view that exceptional responders may hold the key to better understanding drug resistance. This review underscores the importance of polytherapies as an effective means of targeting resistance signalling networks and achieving durable clinical responses in the era of personalised cancer medicine.

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Emerging insights of tumor heterogeneity and drug resistance mechanisms in lung cancer targeted therapy

Journal of Hematology & Oncology ◽

10.1186/s13045-019-0818-2 ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 22

Author(s):

Zuan-Fu Lim ◽

Patrick C. Ma

Keyword(s):

Lung Cancer ◽

Drug Resistance ◽

Targeted Therapy ◽

Minimal Residual Disease ◽

Tumor Heterogeneity ◽

Residual Disease ◽

Resistance Mechanisms ◽

Intratumoral Heterogeneity ◽

Acquired Drug Resistance ◽

Minimal Residual

AbstractThe biggest hurdle to targeted cancer therapy is the inevitable emergence of drug resistance. Tumor cells employ different mechanisms to resist the targeting agent. Most commonly in EGFR-mutant non-small cell lung cancer, secondary resistance mutations on the target kinase domain emerge to diminish the binding affinity of first- and second-generation inhibitors. Other alternative resistance mechanisms include activating complementary bypass pathways and phenotypic transformation. Sequential monotherapies promise to temporarily address the problem of acquired drug resistance, but evidently are limited by the tumor cells’ ability to adapt and evolve new resistance mechanisms to persist in the drug environment. Recent studies have nominated a model of drug resistance and tumor progression under targeted therapy as a result of a small subpopulation of cells being able to endure the drug (minimal residual disease cells) and eventually develop further mutations that allow them to regrow and become the dominant population in the therapy-resistant tumor. This subpopulation of cells appears to have developed through a subclonal event, resulting in driver mutations different from the driver mutation that is tumor-initiating in the most common ancestor. As such, an understanding of intratumoral heterogeneity—the driving force behind minimal residual disease—is vital for the identification of resistance drivers that results from branching evolution. Currently available methods allow for a more comprehensive and holistic analysis of tumor heterogeneity in that issues associated with spatial and temporal heterogeneity can now be properly addressed. This review provides some background regarding intratumoral heterogeneity and how it leads to incomplete molecular response to targeted therapies, and proposes the use of single-cell methods, sequential liquid biopsy, and multiregion sequencing to discover the link between intratumoral heterogeneity and early adaptive drug resistance. In summary, minimal residual disease as a result of intratumoral heterogeneity is the earliest form of acquired drug resistance. Emerging technologies such as liquid biopsy and single-cell methods allow for studying targetable drivers of minimal residual disease and contribute to preemptive combinatorial targeting of both drivers of the tumor and its minimal residual disease cells.

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Model-guided therapy for hepatocellular carcinoma: a role for information technology in predictive, preventive and personalized medicine

The EPMA Journal ◽

10.1186/1878-5085-5-16 ◽

2014 ◽

Vol 5 (1) ◽

Cited By ~ 9

Author(s):

Leonard Berliner ◽

Heinz U Lemke ◽

Eric vanSonnenberg ◽

Hani Ashamalla ◽

Malcolm D Mattes ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Information Technology ◽

Personalized Medicine ◽

Preventive And Personalized Medicine ◽

And Personalized Medicine ◽

Guided Therapy

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Dissecting targeted therapy resistance: Integrating models to quantify environment mediated drug resistance

10.1101/156547 ◽

2017 ◽

Author(s):

Noemi Picco ◽

Erik Sahai ◽

Philip K. Maini ◽

Alexander R. A. Anderson

Keyword(s):

Drug Resistance ◽

Targeted Therapy ◽

Targeted Therapies ◽

Carrying Capacity ◽

Conflicts Of Interest ◽

Treatment Strategies ◽

Model Systems ◽

Significant Heterogeneity

AbstractDrug resistance is the single most important driver of cancer treatment failure for modern targeted therapies. This resistance may be due to the presence of dormant or aggressive tumor cell phenotypes or to context-driven protection. Non-malignant cells and other factors, constituting the microenvironment in which the tumor grows (the stroma), are now thought to play a crucial role in both therapeutic response and resistance. Specifically, the dialogue between the tumor and stroma has been shown to modulate the response to molecularly targeted therapies, through proliferative and survival signaling. The goal of this work is to investigate interactions between a growing tumor and its surrounding stroma in facilitating the emergence of drug resistance. We use mathematical modeling as a theoretical framework to bridge between experimental models and scales, with the aim of separating the intrinsic and extrinsic components of resistance in BRAF mutated melanoma. The model describes tumor-stroma dynamics both with and without treatment. Calibration of our model, through the integration of experimental data, revealed significant variation across animal replicates in either the intensity of stromal promotion or intrinsic tissue carrying capacity. Furthermore our study highlights the need to account for this variation in the design of treatment strategies. Major Findings. Through the integration of a simple mathematical model with in vitro and in vivo experimental growth dynamics of melanoma cell lines (both with and without drug), we were able to dissect the relative contributions of intrinsic versus environmental resistance. Our study revealed significant heterogeneity in vivo, indicating that there is a diversity of either stromal promotion or tumor carrying capacity under targeted therapy. We believe this variation may be one possible explanation for the heterogeneity observed across patients and within individual patients with multiple metastases. Therefore, quantifying this variation both within in vivo model systems and in individual patients could have a significant impact on the design of future treatment strategies that target both the tumor and stroma. Further, we present guidelines for building more effective and longer lasting therapeutic strategies utilizing our experimentally calibrated model. These strategies explicitly consider the protective nature of the stroma and utilize inhibitors that modulate it.PrecisQuantification of the environmental contribution to drug resistance reveals heterogeneity that significantly alters treatment dynamics that can be exploited for therapeutic gain.Financial SupportPicco and Anderson: US National Cancer Institute grant U01CA151924.Picco: UK Engineering and Physical Sciences Research Council (EPSRC grant number EP/G037280/1).Conflict of Interest DisclosureThe authors declare no potential conflicts of interest.

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