Precision oncology for BRAF-mutant cancers with BRAF and MEK inhibitors: from melanoma to tissue-agnostic therapy

Gouda, Mohamed A.; Subbiah, Vivek

doi:10.1016/j.esmoop.2023.100788

Cited by 37 publications

(30 citation statements)

References 81 publications

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“…A disadvantage of this approach, however, is the ectopic overexpression of the oncoprotein in question outside of its histological context. This is particularly important as the histological context, which is mainly defined by the ontogeny of the cancer cell and its tumor microenvironment, is responsible for the contrasting drug responsiveness of various BRAF V600E -driven tumor entities ( 67 , 68 ). Therefore, we assayed the drug responsiveness of three cell lines derived from ovarian (OV-90), non–small cell lung (NCI-H2405), and pancreatic (BxPC3) carcinoma that harbored three distinct endogenous BRAF Δβ3-αC oncoproteins, as we confirmed ourselves (fig.…”

Section: Resultsmentioning

confidence: 99%

BRAF ^Δβ3-αC in-frame deletion mutants differ in their dimerization propensity, HSP90 dependence, and druggability

Lauinger,

Christen,

Klar

et al. 2023

Sci. Adv.

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In-frame BRAF exon 12 deletions are increasingly identified in various tumor types. The resultant BRAF Δβ3-αC oncoproteins usually lack five amino acids in the β3-αC helix linker and sometimes contain de novo insertions. The dimerization status of BRAF Δβ3-αC oncoproteins, their precise pathomechanism, and their direct druggability by RAF inhibitors (RAFi) has been under debate. Here, we functionally characterize BRAF ΔLNVTAP>F and two novel mutants, BRAF delinsFS and BRAF ΔLNVT>F , and compare them with other BRAF Δβ3-αC oncoproteins. We show that BRAF Δβ3-αC oncoproteins not only form stable homodimers and large multiprotein complexes but also require dimerization. Nevertheless, details matter as aromatic amino acids at the deletion junction of some BRAF Δβ3-αC oncoproteins, e.g., BRAF ΔLNVTAP>F , increase their stability and dimerization propensity while conferring resistance to monomer-favoring RAFi such as dabrafenib or HSP 90/CDC37 inhibition. In contrast, dimer-favoring inhibitors such as naporafenib inhibit all BRAF Δβ3-αC mutants in cell lines and patient-derived organoids, suggesting that tumors driven by such oncoproteins are vulnerable to these compounds.

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Section: Resultsmentioning

confidence: 99%

BRAF ^Δβ3-αC in-frame deletion mutants differ in their dimerization propensity, HSP90 dependence, and druggability

Lauinger,

Christen,

Klar

et al. 2023

Sci. Adv.

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“…These studies included historically challenging and treatment‐resistant tumors, such as cholangiocarcinoma, sarcoma, glioma, 37 neuroendocrine carcinoma, and salivary gland carcinoma. This comprehensive collection of data serves as compelling evidence that the application of single‐agent BRAF inhibition extends far beyond the previously acknowledged scope in melanoma, 38,39 exhibiting a much wider range of clinical effectiveness 25,40 …”

Section: Basket Trials Leading To Us Food and Drug Administration App...mentioning

confidence: 92%

“…This comprehensive collection of data serves as compelling evidence that the application of single-agent BRAF inhibition extends far beyond the previously acknowledged scope in melanoma, 38,39 exhibiting a much wider range of clinical effectiveness. 25,40…”

Section: Ve-basket Trial For Targeting Braf V600 Mutations With Braf ...mentioning

confidence: 99%

Revolutionizing cancer drug development: Harnessing the potential of basket trials

Subbiah,

Burris,

Kurzrock

2023

Cancer

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The landscape of cancer therapy has been transformed by advances in clinical next‐generation sequencing, genomically targeted therapies, and immunotherapies. Well designed clinical trials and efficient clinical trial conduct are crucial for advancing our understanding of cancer, improving patient outcomes, and identifying personalized treatments. Basket trials have emerged as one of the efficient modern clinical trial designs that evaluate the efficacy of these therapies across multiple cancer types based on specific molecular alterations or biomarkers, irrespective of histology or anatomic location. This review delves into the evolution of basket trials in cancer drug development, highlighting their potential prospects and current obstacles. The design of basket trials involves screening patients for specific molecular alterations or biomarkers and enrolling them in the trial to receive the targeted therapy under investigation. Statistical considerations play a crucial role in the design, analysis, and interpretation of basket trials. Several notable examples of basket trials that have led to US Food and Drug Administration approval for uncommon molecular alterations (e.g., NTRK fusions, BRAF mutations, RET and FGFR1 alterations) are discussed, including LOXO‐TRK (ClinicalTrials.gov identifier NCT02122913)/SCOUT (ClinicalTrials.gov identifier NCT02637687)/NAVIGATE (ClinicalTrials.gov identifier NCT02576431)/STARTRK (ClinicalTrials.gov identifiers NT02097810, NT02568267), VE‐BASKET (ClinicalTrials.gov identifier NCT01524978), ROAR Basket (ClinicalTrials.gov identifier NCT02034110), LIBRETTO‐001 (ClinicalTrials.gov identifier NCT03157128), ARROW (ClinicalTrials.gov identifier NCT03037385), FIGHT‐203 (ClinicalTrials.gov identifier NCT03011372), and the National Cancer Institute‐Molecular Analysis for Therapy Choice trial (ClinicalTrials.gov identifier NCT02465060). Basket trials have the potential to revolutionize cancer treatment by identifying effective therapies for patients based on specific molecular alterations or biomarkers rather than traditional histology‐based approaches.Plain Language Summary To gain more knowledge about cancer, improve patient outcomes, and discover personalized treatments, it is crucial to conduct clinical trials efficiently. One effective type of clinical trial is called a basket trial. In basket trials, new treatments are tested on various types of cancer, regardless of their location in the body; instead, researchers focus on specific abnormalities in the cancer cells. Basket trials offer hope that we can find personalized treatments that are more effective for each individual battling cancer.

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“…4 Furthermore, targeted therapies, like BRAF inhibitors and smoothened inhibitors, have also been developed to inhibit molecular targets of skin cancer. 5,6 However, more than half of skin cancer patients remain unresponsive to current treatments, and there are types of skin cancers that are not currently receptive to these emerging treatment modalities, like advanced melanoma and Merkle cell carcinoma. 7,8 These challenges are likely attributed to the complexity of skin cancer pathogenesis, involving an intricate interplay among genetic mutations, immune responses and external influences, like sun exposure.…”

mentioning

confidence: 99%

“…14 Lastly, melanoma has been tied to various mutations, with the BRAFV600E mutation standing out as a particularly significant oncogenic alteration that fuels melanoma carcinogenesis. 5 Hence, systems genetics may be utilized to deepen our comprehension of the interplay between genetic mutations and other co-regulated genes within the context of various skin cancer types, broadening our perspective beyond a single gene locus.…”

mentioning

confidence: 99%

Utilizing systems genetics to enhance understanding into molecular targets of skin cancer

Kim,

Kulkarni,

Goode

et al. 2024

Experimental Dermatology

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Despite progress made with immune checkpoint inhibitors and targeted therapies, skin cancer remains a significant public health concern in the United States. The intricacies of the disease, encompassing genetics, immune responses, and external factors, call for a comprehensive approach. Techniques in systems genetics, including transcriptional correlation analysis, functional pathway enrichment analysis, and protein–protein interaction network analysis, prove valuable in deciphering intricate molecular mechanisms and identifying potential diagnostic and therapeutic targets for skin cancer. Recent studies demonstrate the efficacy of these techniques in uncovering molecular processes and pinpointing diagnostic markers for various skin cancer types, highlighting the potential of systems genetics in advancing innovative therapies. While certain limitations exist, such as generalizability and contextualization of external factors, the ongoing progress in AI technologies provides hope in overcoming these challenges. By providing protocols and a practical example involving Braf, we aim to inspire early‐career experimental dermatologists to adopt these tools and seamlessly integrate these techniques into their skin cancer research, positioning them at the forefront of innovative approaches in combating this devastating disease.

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Precision oncology for BRAF-mutant cancers with BRAF and MEK inhibitors: from melanoma to tissue-agnostic therapy

Cited by 37 publications

References 81 publications

BRAF ^Δβ3-αC in-frame deletion mutants differ in their dimerization propensity, HSP90 dependence, and druggability

BRAF ^Δβ3-αC in-frame deletion mutants differ in their dimerization propensity, HSP90 dependence, and druggability

Revolutionizing cancer drug development: Harnessing the potential of basket trials

Utilizing systems genetics to enhance understanding into molecular targets of skin cancer

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Precision oncology for BRAF-mutant cancers with BRAF and MEK inhibitors: from melanoma to tissue-agnostic therapy

Cited by 37 publications

References 81 publications

BRAF Δβ3-αC in-frame deletion mutants differ in their dimerization propensity, HSP90 dependence, and druggability

BRAF Δβ3-αC in-frame deletion mutants differ in their dimerization propensity, HSP90 dependence, and druggability

Revolutionizing cancer drug development: Harnessing the potential of basket trials

Utilizing systems genetics to enhance understanding into molecular targets of skin cancer

Contact Info

Product

Resources

About

BRAF ^Δβ3-αC in-frame deletion mutants differ in their dimerization propensity, HSP90 dependence, and druggability

BRAF ^Δβ3-αC in-frame deletion mutants differ in their dimerization propensity, HSP90 dependence, and druggability