Tumor mutational burden for the prediction of PD-(L)1 blockade efficacy in cancer: challenges and opportunities.

Tumor mutational burden (TMB) is a biomarker that measures the number of somatic mutations in a tumor's genome. TMB has emerged as a predictor of response to immune checkpoint inhibitors (ICIs) in various cancer types, and several studies have shown that patients with high TMB have better outcomes when treated with programmed death-ligand 1-based therapies. Recently, the Food and Drug Administration has approved TMB as a companion diagnostic for the use of pembrolizumab in solid tumors. However, despite its potential, the use of TMB as a biomarker for immunotherapy efficacy is limited by several factors. Here we review the limitations of TMB in predicting immunotherapy outcomes in patients with cancer and discuss potential strategies to optimize its use in the clinic.

Circulating tumor DNA and radiological tumor volume identify patients at risk for relapse with resected, early-stage non-small-cell lung cancer.

BACKGROUND: Predicting relapse and overall survival (OS) in early-stage non-small-cell lung cancer (NSCLC) patients remains challenging. Therefore, we hypothesized that detection of circulating tumor DNA (ctDNA) can identify patients with increased risk of relapse and that integrating radiological tumor volume measurement along with ctDNA detectability improves prediction of outcome. PATIENTS AND METHODS: We analyzed 366 serial plasma samples from 85 patients who underwent surgical resections and assessed ctDNA using a next-generation sequencing liquid biopsy assay, and measured tumor volume using a computed tomography-based three-dimensional annotation. RESULTS: Our results showed that patients with detectable ctDNA at baseline or after treatment and patients who did not clear ctDNA after treatment had a significantly worse clinical outcome. Integrating radiological analysis allowed the stratification in risk groups prognostic of clinical outcome as confirmed in an independent cohort of 32 patients. CONCLUSIONS: Our findings suggest ctDNA and radiological monitoring could be valuable tools for guiding follow-up care and treatment decisions for early-stage NSCLC patients.

Molecular markers of metastatic disease in KRAS-mutant lung adenocarcinoma.

BACKGROUND: Prior studies characterized the association of molecular alterations with treatment-specific outcomes in KRAS-mutant (KRASMUT) lung adenocarcinoma (LUAD). Less is known about the prognostic role of molecular alterations and their associations with metastatic disease. PATIENTS AND METHODS: We analyzed clinicogenomic data from 1817 patients with KRASMUT LUAD sequenced at the Dana-Farber Cancer Institute (DFCI) and Memorial Sloan Kettering Cancer Center (MSKCC). Patients with metastatic (M1) and nonmetastatic (M0) disease were compared. Transcriptomic data from The Cancer Genome Atlas (TCGA) were investigated to characterize the biology of differential associations with clinical outcomes. Organ-specific metastasis was associated with overall survival (OS). RESULTS: KEAP1 (DFCI: OR = 2.3, q = 0.04; MSKCC: OR = 2.2, q = 0.00027) and SMARCA4 mutations (DFCI: OR = 2.5, q = 0.06; MSKCC: OR = 2.6, q = 0.0021) were enriched in M1 versus M0 tumors. On integrative modeling, NRF2 activation was the genomic feature most associated with OS. KEAP1 mutations were enriched in M1 versus M0 tumors independent of STK11 status (KEAP1MUT/STK11WT: DFCI OR = 3.0, P = 0.0064; MSKCC OR = 2.0, P = 0.041; KEAP1MUT/STK11MUT: DFCI OR = 2.3, P = 0.0063; MSKCC OR = 2.5, P = 3.6 × 10-05); STK11 mutations without KEAP1 loss were not associated with stage (KEAP1WT/STK11MUT: DFCI OR = 0.97, P = 1.0; MSKCC OR = 1.2, P = 0.33) or outcome. KEAP1/KRAS-mutated tumors with and without STK11 mutations exhibited high functional STK11 loss. The negative effects of KEAP1 were compounded in the presence of bone (HR = 2.3, P = 4.4 × 10-14) and negated in the presence of lymph node metastasis (HR = 1.0, P = 0.91). CONCLUSIONS: Mutations in KEAP1 and SMARCA4, but not STK11, were associated with metastatic disease and poor OS. Functional STK11 loss, however, may contribute to poor outcomes in KEAP1MUT tumors. Integrating molecular data with clinical and metastatic-site annotations can more accurately risk stratify patients.

Metabolic pathway alterations that support cell proliferation.

Proliferating cells adapt metabolism to support the conversion of available nutrients into biomass. How cell metabolism is regulated to balance the production of ATP, metabolite building blocks, and reducing equivalents remains uncertain. Proliferative metabolism often involves an increased rate of glycolysis. A key regulated step in glycolysis is catalyzed by pyruvate kinase to convert phosphoenolpyruvate (PEP) to pyruvate. Surprisingly, there is strong selection for expression of the less active M2 isoform of pyruvate kinase (PKM2) in tumors and other proliferative tissues. Cell growth signals further decrease PKM2 activity, and cells with less active PKM2 use another pathway with separate regulatory properties to convert PEP to pyruvate. One consequence of using this alternative pathway is an accumulation of 3-phosphoglycerate (3PG) that leads to the diversion of 3PG into the serine biosynthesis pathway. In fact, in some cancers a substantial portion of the total glucose flux is directed toward serine synthesis, and genetic evidence suggests that glucose flux into this pathway can promote cell transformation. Environmental conditions can also influence the pathways that cells use to generate biomass with the source of carbon for lipid synthesis changing based on oxygen availability. Together, these findings argue that distinct metabolic phenotypes exist among proliferating cells, and both genetic and environmental factors influence how metabolism is regulated to support cell growth.