TP53 mutations predict for poor outcomes in patients with newly diagnosed aggressive B-cell lymphomas in the current era.

Genetic subgroups of diffuse large B-cell lymphoma (DLBCL) have been identified through comprehensive genomic analysis; however, it is unclear whether this can be applied in clinical practice. We assessed whether mutations detected by clinical laboratory mutation analysis (CLMA) were predictive of outcomes in patients with newly diagnosed DLBCL/high-grade B-cell lymphoma (HGBL). Patients diagnosed from 2018 to 2022 whose biopsy samples were subjected to CLMA and who received rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone or rituximab plus etoposide, prednisolone, vincristine, cyclophosphamide, and doxorubicin were analyzed for overall/complete response rate (ORR/CRR) and estimated progression-free/overall survival (PFS/OS). CLMA was successfully performed in 117 of 122 patient samples (96%), with a median turnaround time of 17 days. Median duration of follow-up was 31.3 months. Of the mutations detected in ≥10% of the samples, only TP53 was associated with both progression and death at 2 years. TP53 mutations were detected in 36% of tumors, and patients with TP53 mutations experienced significantly lower ORR (71% vs 90%; P = .009), CRR (55% vs 77%; P = .01), 2-year PFS (57% vs 77%; P = .006), 2-year OS (70% vs 91%; P = .001), and median OS after relapse (6.1 months vs not yet reached; P = .001) as than those without TP53 mutations. Furthermore, patients with TP53 loss-of-function (LOF) mutations experienced lower rates of 2-year PFS/OS than those with non-LOF mutations and inferior or near-inferior 2-year PFS if harboring high-risk clinicopathologic features. TP53 mutations identified through CLMA can predict for inferior outcomes in patients with newly diagnosed DLBCL/HGBL. Results of CLMA can be used in real time to inform prognosis and/or identify candidates for clinical trials.

Genomic landscape of metastatic breast cancer identifies preferentially dysregulated pathways and targets.

Nearly all breast cancer deaths result from metastatic disease. Despite this, the genomic events that drive metastatic recurrence are poorly understood. We performed whole-exome and shallow whole-genome sequencing to identify genes and pathways preferentially mutated or copy-number altered in metastases compared with the paired primary tumors from which they arose. Seven genes were preferentially mutated in metastases - MYLK, PEAK1, SLC2A4RG, EVC2, XIRP2, PALB2, and ESR1 - 5 of which are not significantly mutated in any type of human primary cancer. Four regions were preferentially copy-number altered: loss of STK11 and CDKN2A/B, as well as gain of PTK6 and the membrane-bound progesterone receptor, PAQR8. PAQR8 gain was mutually exclusive with mutations in the nuclear estrogen and progesterone receptors, suggesting a role in treatment resistance. Several pathways were preferentially mutated or altered in metastases, including mTOR, CDK/RB, cAMP/PKA, WNT, HKMT, and focal adhesion. Immunohistochemical analyses revealed that metastases preferentially inactivate pRB, upregulate the mTORC1 and WNT signaling pathways, and exhibit nuclear localization of activated PKA. Our findings identify multiple therapeutic targets in metastatic recurrence that are not significantly mutated in primary cancers, implicate membrane progesterone signaling and nuclear PKA in metastatic recurrence, and provide genomic bases for the efficacy of mTORC1, CDK4/6, and PARP inhibitors in metastatic breast cancer.

Clinical activity of the EGFR tyrosine kinase inhibitor osimertinib in EGFR-mutant glioblastoma.

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults and carries a dismal prognosis. The EGFR gene is among the most commonly deranged genes in GBM and thus an important therapeutic target. We report the case of a young female with heavily pretreated EGFR-mutated GBM, for whom we initiated osimertinib, an oral, third-generation tyrosine kinase inhibitor that irreversibly inhibits EGFR and has significant brain penetration. We then review some of the main challenges in targeting EGFR, including lack of central nervous system penetration with most tyrosine kinase inhibitors, molecular heterogeneity of GBM and the need for enhanced specificity for the EGFR mutations relevant in GBM.

Co-targeting BET and MEK as salvage therapy for MAPK and checkpoint inhibitor-resistant melanoma.

Despite novel therapies for melanoma, drug resistance remains a significant hurdle to achieving optimal responses. NRAS-mutant melanoma is an archetype of therapeutic challenges in the field, which we used to test drug combinations to avert drug resistance. We show that BET proteins are overexpressed in NRAS-mutant melanoma and that high levels of the BET family member BRD4 are associated with poor patient survival. Combining BET and MEK inhibitors synergistically curbed the growth of NRAS-mutant melanoma and prolonged the survival of mice bearing tumors refractory to MAPK inhibitors and immunotherapy. Transcriptomic and proteomic analysis revealed that combining BET and MEK inhibitors mitigates a MAPK and checkpoint inhibitor resistance transcriptional signature, downregulates the transcription factor TCF19, and induces apoptosis. Our studies demonstrate that co-targeting MEK and BET can offset therapy resistance, offering a salvage strategy for melanomas with no other therapeutic options, and possibly other treatment-resistant tumor types.

Next-generation sequencing and personalized genomic medicine in hepatobiliary malignancies.

Liver cancer is a heterogeneous group of tumors characterized by significant molecular and genomic heterogeneity. The advent of powerful genomic technologies has allowed detection of recurrent somatic alterations in liver cancer, including mutations, copy number alterations as well as changes in transcriptomes and epigenomes, with the potential to translate these data into clinically relevant predictive and prognostic factors. In this review, we discuss recent advances in the application of high-throughput genomic technologies in liver cancer and the integration of such cancer genome profiling data, highlighting specific relevant subgroups and explain how this knowledge can be used in translational clinical research, 'basket trials', molecular tumor boards, targeted therapy and for personalized genomic medicine applications.