Impact of renal cell carcinoma molecular subtypes on immunotherapy and targeted therapy outcomes.

Saliby et al. show that a machine learning approach can accurately classify clear cell renal cell carcinoma (RCC) into distinct molecular subtypes using transcriptomic data. When applied to tumors biospecimens from the JAVELIN Renal 101 (JR101) trial, a benefit is observed with immune checkpoint inhibitor (ICI)-based therapy across all molecular subtypes.

Circulating and Intratumoral Immune Determinants of Response to Atezolizumab plus Bevacizumab in Patients with Variant Histology or Sarcomatoid Renal Cell Carcinoma.

Renal cell carcinoma (RCC) of variant histology comprises approximately 20% of kidney cancer diagnoses, yet the optimal therapy for these patients and the factors that impact immunotherapy response remain largely unknown. To better understand the determinants of immunotherapy response in this population, we characterized blood- and tissue-based immune markers for patients with variant histology RCC, or any RCC histology with sarcomatoid differentiation, enrolled in a phase II clinical trial of atezolizumab and bevacizumab. Baseline circulating (plasma) inflammatory cytokines were highly correlated with one another, forming an "inflammatory module" that was increased in International Metastatic RCC Database Consortium poor-risk patients and was associated with worse progression-free survival (PFS; P = 0.028). At baseline, an elevated circulating vascular endothelial growth factor A (VEGF-A) level was associated with a lack of response (P = 0.03) and worse PFS (P = 0.021). However, a larger increase in on-treatment levels of circulating VEGF-A was associated with clinical benefit (P = 0.01) and improved overall survival (P = 0.0058). Among peripheral immune cell populations, an on-treatment decrease in circulating PD-L1+ T cells was associated with improved outcomes, with a reduction in CD4+PD-L1+ [HR, 0.62; 95% confidence interval (CI), 0.49-0.91; P = 0.016] and CD8+PD-L1+ T cells (HR, 0.59; 95% CI, 0.39-0.87; P = 0.009) correlated with improved PFS. Within the tumor itself, a higher percentage of terminally exhausted (PD-1+ and either TIM-3+ or LAG-3+) CD8+ T cells was associated with worse PFS (P = 0.028). Overall, these findings support the value of tumor and blood-based immune assessments in determining therapeutic benefit for patients with RCC receiving atezolizumab plus bevacizumab and provide a foundation for future biomarker studies for patients with variant histology RCC receiving immunotherapy-based combinations.

A proteogenomic portrait of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma.

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.

TMT Labeling for the Masses: A Robust and Cost-efficient, In-solution Labeling Approach.

Isobaric stable isotope labeling using, for example, tandem mass tags (TMTs) is increasingly being applied for large-scale proteomic studies. Experiments focusing on proteoform analysis in drug time course or perturbation studies or in large patient cohorts greatly benefit from the reproducible quantification of single peptides across samples. However, such studies often require labeling of hundreds of micrograms of peptides such that the cost for labeling reagents represents a major contribution to the overall cost of an experiment. Here, we describe and evaluate a robust and cost-effective protocol for TMT labeling that reduces the quantity of required labeling reagent by a factor of eight and achieves complete labeling. Under- and overlabeling of peptides derived from complex digests of tissues and cell lines were systematically evaluated using peptide quantities of between 12.5 and 800 µg and TMT-to-peptide ratios (wt/wt) ranging from 8:1 to 1:2 at different TMT and peptide concentrations. When reaction volumes were reduced to maintain TMT and peptide concentrations of at least 10 mm and 2 g/l, respectively, TMT-to-peptide ratios as low as 1:1 (wt/wt) resulted in labeling efficiencies of > 99% and excellent intra- and interlaboratory reproducibility. The utility of the optimized protocol was further demonstrated in a deep-scale proteome and phosphoproteome analysis of patient-derived xenograft tumor tissue benchmarked against the labeling procedure recommended by the TMT vendor. Finally, we discuss the impact of labeling reaction parameters for N-hydroxysuccinimide ester-based chemistry and provide guidance on adopting efficient labeling protocols for different peptide quantities.