TMB: a promising immune-response biomarker, and potential spearhead in advancing targeted therapy trials


Immune checkpoint inhibition (ICI) has revolutionized cancer treatment, and produced durable responses in many cancer types. However, there remains a subset of patients that do not respond despite their tumors exhibiting PD-L1 expression, which highlights the need for additional biomarkers relevant to response. Here, we review checkpoint inhibitor signal pathways, resistance and sensitivity mechanisms, as well as response rates. We also investigate the correlation and response to ICI with BRCA1/2 mutation status and homologous recombination deficient tumors. Collectively we show that the use of tumor mutational burden may be effective as an emerging biomarker.


Tumor mutational burden

Highly mutated tumors are thought to harbor an increased neo-antigen burden, making them immunogenic, and consequently more responsive to immunotherapy [1,2,3]. It is based on this postulation that tumor mutational burden (TMB), which quantifies the total number of mutations present in a tumor specimen, has emerged as a promising quantitative genomic biomarker for response to immunotherapy, independent of micro-satellite instability (MSI) or PD-L1 expression status [4,5,6,7,8,9].

As a genomically driven disease, cancer cells acquire genetic somatic alterations that differ from the host’s germ line, conferring survival abilities and ability to evade proliferation control mechanisms [10]. These somatic mutations in coding regions of the genome (non-synonymous mutations and, more significantly, frame shifts) potentially result in new or fragmented proteins/peptides (neo-antigens), which can be recognized as “non-self”, and elicit an antitumor immune response [3, 10,11,12]. Neo-antigens are subsequently thought to increase the immune cell repertoire and enhance the clinical efficacy of immune checkpoint inhibitors [11, 13]. Exposure to exogenous mutagenic factors, or intrinsic compounding genetic defects such as DNA mismatch repair deficiency or MSI, significantly increase the rate of these somatic mutations [4, 14, 15], eventually resulting in a propagation of this mutational “signature” or burden with clonal expansion and tumor evolution [2, 16]. In fact, TMB varies across different types of tumors with higher average TMB values in cancers exposed to highly mutagenic agents, such as lung, bladder (smoking) and melanoma (UV) compared to other tumor types [12, 17].

Technically, the assessment of TMB involves next-generation sequencing (NGS) of tumor samples [4]. While initial exploratory studies of TMB were carried via whole exon sequencing (WES), more recent and targeted cancer gene panels for TMB assessment have emerged [4, 15, 18, 19]: WES comprehensively surveys the entire coding regions of around 22,000 human genes for genomic alterations [20, 21]. Alternatively, the FoundationOne® CDx is a 324-gene panel assay that is FDA approved for profiling of mutations in solid tumors and assessment of genomic signatures, including TMB and MSI. Similarly, the 468-gene panel assay MSK-IMPACTTM and the Illumina’s TruSight Oncology 500 assay, both capture TMB. TMB is typically reported as the total number of mutations per tumor when assessed by WES, or as normalized to mutations per megabase (mut/Mb) by gene panel assays [4, 15, 22, 23]. However, the precise calculation varies between assays depending on the sequenced region, types of mutations included, as does the cut-off to define high vs. low TMB vary between studies (upper tertile vs. quartile vs. median vs. 10 mut/Mb, as possible cut-offs).

There are currently no indicated therapies that specifically target TMB status, or use it as a companion biomarker to direct therapy despite a substantial amount of data from many key studies that strongly support a promising role for TMB status as an emerging biomarker for checkpoint inhibitors cancer therapy.

Checkpoint inhibitor therapies: indications, signal pathway, sensitivity/resistance, and response rates in indicated population

There are currently seven US FDA-approved immune checkpoint inhibitors that are approved for treatment of eight histological types of solid malignancies, including: (i) skin cancers (malignant melanoma, merkel cell carcinoma and cutaneous squamous cell carcinoma), (ii) lung cancer (non-small and small cell lung carcinoma), (iii) hepatocellular carcinoma, (iv) genitourinary cancers (metastatic urothelial carcinoma and cervical cancer), (v) head and neck squamous cell carcinoma, (vi) gastric and gastro-esophageal junction adenocarcinoma, (vii) renal cell carcinoma, and (viii) triple-negative breast cancer (TNBC) (reviewed in ref. [24]). Of particular interest for this review is the additional approval of select checkpoint inhibitors for treatment of MSI-high cancers and for cancer deficient in MisMatch Repair (dMMR) regardless of cancer histology [25]. Immune checkpoint inhibitors are classified into three categories depending on the receptor/ligand they target: Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4), Programmed cell Death-1 (PD-1), and Programmed Death Ligand-1 (PD-L1).

Drug indications

Ipilimumab (Yervoy®) is the first approved immunotherapy and the only anti-CTLA4 inhibitor. It is approved for metastatic and non-resectable melanoma in adult and pediatric (≥12 years old) patients as well as patients with advanced renal cell carcinoma (RCC; in combination with nivolumab) and in MSI-H and dMMR metastatic colorectal cancer (mCRC) that progressed following treatment with fluoropyrimidine, oxaliplatin and irinotecan (in combination with nivolumab) [26]. Pembrolizumab (Keytruda®), Nivolumab (Opdivo®) and Cemiplimab (Libtayo®) are anti-PD1 blocking antibodies. Pembrolizumab is indicated for treatment of wide variety of solid tumors (reviewed in ref. [27]): in patients with unresectable or metastatic melanoma, for the adjuvant treatment of loco-regional recurrence of melanoma following complete resection, and for patients with recurrent locally advanced or metastatic Merkel cell carcinoma (MCC). It is also approved for treatment of metastatic non-squamous non-small cell lung cancer (NSCLC) with no EGFR or ALK genomic aberrations in combination with first line chemotherapy. As a single agent, pembrolizumab is indicated for first-line therapy of metastatic NSCLC with high PD-L1 expression (Tumor Proportion Score TPS ≥ 50%) in patients with no EGFR or ALK aberrations, or in those with low PD-L1 (TPS ≥ 1%) but positive for EGFR and ALK genomic aberrations. Pembrolizumab is further approved, within certain indications, in recurrent or metastatic head and neck squamous cell cancer (HNSCC), metastatic urothelial carcinoma (mUC), gastric or gastro-esophageal junction GEJ) adenocarcinoma, recurrent/metastatic cervical cancer, and lastly, in hepatocellulcar carcinoma (HCC) [27]. Nivolumab (Opdivo®) is another anti-PD1 checkpoint inhibitor (indications reviewed in ref. [28]). In melanoma, nivolumab is indicated as a single agent for patients with BRAF-V600 wild type (WT) or mutated (mut) unresectable or metastatic melanoma, as well as in the adjuvant setting for patients with loco-regional or metastatic disease recurrence following complete resection. Unlike pembrolizumab, nivolumab is indicated in patients with metastatic NSCLC and progression on/after chemotherapy regardless of PD-L1 levels. Nivolumab is further approved, within certain indications, for treatment of patients with RCC, HNSCC, MSI-H/dMMR mCRC (as a single agent or in combination with ipilimumab), and in HCC [28]. Cemiplimab (Libtayo®) is the most recently approved PD-1 monoclonal antibody (September 2018), and is indicated for the treatment of metastatic cutaneous squamous cell carcinoma (CSCC) and locally advanced CSCC that are not amenable for curative surgery or curative radiation [29]. Atezolizumab (Tecentriq®), Avelumab (Bavencio®) and Durvalumab (Imfinzi®) are all PD-L1 inhibitors. Atezolizumab is approved for the treatment of adult patients with locally advanced and mUC who are not eligible for chemotherapy or have shown progression while on treatment. In NSCLC, it is indicated as first line therapy with chemotherapy in adult patients with metastatic NSCLC with no EGFR/ALK aberrations, or in those who progressed during/following therapy regardless of EGFR/ALK status. Lastly, atezolizumab is indicated in TNBC patients in combination with chemotherapy in unresectable or metastatic tumors that express PD-L1 [30]. Both avelumab and durvalumab are indicated in patients with metastatic MCC, and locally advanced or mUC with disease progression during, following, or within 12 months of neo-adjuvant or adjuvant chemotherapy [31, 32]. Durvalumab, is additionally indicated as a maintenance therapy for unresectable, Stage III NSCLC without disease progression following chemo-radiation therapy [32].

Signal pathway, sensitivity, and resistance mechanisms to checkpoint inhibitors therapy

Checkpoint inhibitors are monoclonal antibodies that block the interaction between PD-1 and CTLA-4 on the one hand, and their ligands—PD-L1 and CD80/86, respectively—on the other hand. Under physiological conditions, immune checkpoints modulate both the duration and amplitude of immune responses, thus avoiding over-activation of T cells-mediated immune response [33]: PD-1 and CTLA-4 receptors are mainly expressed on activated CD8+ T-cells (in addition to CD4, B cells and dendritic cells), while their respective ligands, PD-L1 and CD80/86 are expressed on antigen-presenting cells (APCs) [34, 35]. Following engagement of receptors and ligands, the receptors transmit inhibitory signals that abrogate T-cell receptor (TCR)-mediated activating signals, thus preventing further antigen-mediated T-cell activation, and consequently limiting T-cell responsiveness [36]. This pathway is often “hijacked” by tumor cells, many of which express PD-L1 and CD80/86 either constitutively [37, 38], or as a reaction to inflammatory cytokines (interferons) and tumor necrosis factor alpha, resulting in inhibition of immune-mediated response against tumor cells [39, 40]. As such, therapeutic checkpoint inhibitors against either the receptors (PD-1 and CTLA-4) or the ligands (PD-L1), block this interaction, release T-cells from their “exhausted” non-functional state, allow reactivation and clonal-proliferation of antigen-experienced T cells in the tumor micro-environment (TME), and re-invigorate tumor antigen-specific immunity [40,41,42].

Successful anti-tumor immune response to checkpoint blockage is primarily dictated by the sensitivity of the immune system to tumor-associated antigens (TAA). The generation of tumor-reactive CD8+ T cells requires successful processing and presentation of tumor-associated peptide antigens by APCs, and the subsequent recognition of these peptides displayed by major histo-compatibility complexes I and II (MHC I/II): following recognition, engagement of co-stimulatory CD28 receptor on T cells by B7 on APC results in full tumor-specific reactivation, clonal expansion and traffic to EMT of CD8+ T cells which differentiate into effector T-cells that ultimately kill tumor cells displaying TAA via cytolytic effects (granzyme A/B and perforin) [3, 41]. T cells further differentiate into memory T cells, thus providing long-term immunologic memory, positive response to re-challenge with antigen, and presumably a durable disease control [43,44,45].

In light of our understanding of the immune checkpoints signalling pathway, the inhibitors mechanism of action, and tumor sensitivity to immune therapy, the mechanisms of resistance basically function to counter-act the activity of tumor-specific cells at different steps of the signalling pathway (reviewed in [41]). Whether tumor-intrinsic (genetic/epigenetic changes altering either neo-antigen formation/presentation, or downstream cytotoxic T-cells activation pathway), or extrinsic (TME, systemic influences), resistance to checkpoint inhibitors therapy affect either: (i) tumor immunogenicity and antigen presentation, (ii) generation of effector T-cells, or (iii) adequate anti-tumor T-cell function [36, 41, 46]. As described earlier, the efficacy of checkpoint inhibition therapy is largely dependent on the existence of both TAAs and antigen-specific T-cells within tumor tissue. Such TAAs are derived from non-mutated proteins to which T-cell tolerance is incomplete, along with viral antigens (when malignancy is promoted by viral infection), or non-synonymous mutations [3, 17, 47]. TMB is an accurate, tumor-intrinsic feature that correlates with tumor immunogenicity, and TMB has been shown to strongly correlate with immunogenicity and sensitivity to checkpoint inhibition therapy [48, 49]. As such, not all tumor types intrinsically exhibit a higher TMB: RCC, melanoma, and NSCLC for example, express higher TMB while pancreatic or prostate tumors do not, thus exhibiting intrinsic resistance to immune therapy [3, 50, 51]. Loss of tumor immunogenicity can also be acquired in patients who initially responded to checkpoint inhibitors, and later developed resistance [52, 53]: this acquired resistance occurs through a number of pathways, and through molecular and genomic modulations of response to immune blockade such as antigen loss, down-regulation of MHC-I via β-2 mutations, and inactivation of JAK/STAT pathway (and consequent decreased response to IFN) (reviewed in [54]). The use of TMB as an indicator of sensitivity thus becomes relevant in assessing both intrinsic and acquired resistance to checkpoint inhibitors therapy. Proper intra-tumoral infiltration is also essential for successful immune blockade therapy. Loss of PTEN for example increases the levels of CCL2 and VEGF, ultimately decreasing T-cell infiltration and resulting in resistance to therapy [54,55,56]. Similarly, alterations in β-catenin/WNT signalling causes decrease in CCL4 production and a subsequent diminished infiltration of dendritic cells (DCs), poor APC functions, and impaired immune response [57]. Mutations in oncogenic pathways, such as the loss of SKT11/LKB1 in KRAS-mutated tumors, also contribute to decreased T-cell infiltration [54, 58]. Generation of active effector T-cells depends on the effective engagement between an APC (DC) and TCRs on T-cells in secondary lymphoid organs. Mechanisms that perturb DCs migration have been shown to mediate resistance to immune blockade therapy: for example, reduced CCL4 expression reduces DCs migration, leading to resistance to anti-PD1 treatment in human melanoma [57]. Similarly, mice with poor intra-tumoral DCs infiltration exhibited poor response to anti-PD1 and anti-CTLA4 therapies [57, 59, 60]. Incomplete DC maturation secondary to over-expression of VEGF and NFκB-dependent pathways, or to increased TGF- and IL-10 levels, also mediate resistance to checkpoint inhibition therapy [61,62,63]. Resistance can also be mediated by nullification of tumor-specific T-cells function, despite successful initial activation. Factors intrinsic to tumor cells, and other extrinsic ones found in the TME, act to “dampen” the activity of T-cells [42, 64]. These include mutations in key effector pathways (such as JAK1/2), increased levels of PD-L1 and CTLA-4 expression (exhaustion), as well as increased expression of co-inhibitory receptors on T-cells (TIM-3, LAG-3, ITIM), or high levels of immune suppressive cytokines (reviewed in refs. [33, 41]). The TME further contributes to mediating immune escape following up-regulation of oncogenic signaling [65]. These include increased regulatory T-cells (Treg) infiltration of tumor tissue (and the resulting suppression of effector T-cells), increased Myeloid-derived suppressor cells (MDSCs), and M2 tumor-associated macrophages, all through increased TME expression of cytokines and chemokines (CCL5, CCL17, CCL22, CXCL8, and CXCL12) that play a role in recruiting these immune-inhibitory cells to the tumor [66,67,68,69,70].

Response rates in indicated populations

Within the indicated populations described above response rates (objective response rate: ORR; Complete response rate: CRR, and Overall survival: OS in months) have been reported from the major trials that led to each drug’s population-specific FDA approval. For nivolumab, ORR range between 13.3% and 43.7%, while CRR were shown in up to 8.9% of participating subjects, and an OS up to 20 months has been demonstrated [71,72,73,74]. These trials involved participants with melanoma, RCC, HNSCC, NSCLC and platinum-resistant ovarian cancer. Trials involving pembrolizumab, have demonstrated ORRs of 18–40%, CRR up to 16% and similar OS period up to 20 months in melanoma, MCC, NSCLC and mCRC [75,76,77,78,79]. Atezolizumab has shown slightly lower response rates, with ORR of 18–23%, CRR of 2–9% and OS up to 15.6 months [80, 81]. Lastly, response rates to ipilimumab are consistent with an ORR ranging between 11.9% and 19%, and CRR up to 22% [71, 82, 83]. In combination with nivolumab, ipilimumab has shown ORR ranging between 23% and 57.6%, CRR up to 11.5% and OS of 7.7 months in melanoma and NSLC [71, 84].

Despite promising response rates compared to traditional non-immune therapies and despite durable responses, it is clear that not all patients respond to immune checkpoint blockade therapy. While the different mechanisms of resistance partially explain the lack of response in a considerable portion of patients within the indicated population, proper selection of patients that are more likely to respond to immune therapy not only explains poor response within these populations, but can also expand indications of immune therapy beyond the current, histology-defined populations. For example, the use of PD-L1 expression as a biomarker has its own limitations and defects: such levels may change over time, or change based on prior treatments or anatomical sites. Use of small biopsy specimens obtained by fine needle may miss some PD-L1 expression and tumors. Furthermore, antibodies used to detect PD-L1 status in immuno-histochemical (IHC) assays have different affinities and specificities partly due to differences in kits that are used (reviewed in refs. [85, 86]). In fact, data extracted from trials have demonstrated that patients with negative PD-L1 expression could still have favorable outcomes [87, 88]. In NSCLC for example, PD-L1 negative tumors still showed an aggregate 15% response rate [89, 90]. Based on current indications, such patients can easily be denied treatment with pembrolizumab for example, especially if ALK and EGFR are non-mutated. Accordingly, PD-L1 expression may thus not be a preferable biomarker to predict the response to PD-1/PD-L1 inhibition.

TMB and response to immune checkpoint blockade within and outside indicated populations

Early in 2014, our team managed a patient [91] with metastatic endometrial cancer who had failed all standard of care and a PD-L1 of <1% suggesting poor checkpoint inhibitor sensitivity. However, the patient’s TMB was over 20 mutations/mb DNA thereby supporting immune response sensitivity. We elected to manage the patient with Durvalumab via a clinical trial opportunity. The patient had a dramatic prolonged response highlighting possibly a more clinically relevant role of TMB over PD-L1 level in this case.

TMB has evolved as a relevant potential tool and biomarker to identify patients likely to respond to immune checkpoint therapy, across various tumor types and histologies [92]. Given our understanding of TMB’s underlying molecular significance and the greater probability of displaying tumor neo-antigens on the surface of tumor cells, it can be safely hypothesized that tumors with the highest TMB may be more likely to respond to immune therapy by mere increase in the likelihood of neo-antigen recognition by T-cells. In fact, several studies have demonstrated an association between high TMB and response to anti-CTLA-4 or anti-PD-1 [8, 48, 49]. In this section, we review the correlation between high TMB levels and response to immune checkpoint blockade both within and outside indicated populations.

High TMB and response to checkpoint inhibitor therapy within indicated populations

several key studies and data derived from major trials have investigated the relationship between high TMB and response to checkpoint inhibitors in populations for which these therapies are currently indicated. A study conducted by Yarchoan et al. [93], investigated the ORR for anti-PD1 and anti-PD-L1 therapy (nivolumab, pembrolizumab, atezolizumab, durvalumab, avelumab, and cemiplimab) across TMB levels in a large subset of patients (n = 1569) with the following solid tumor types: NSCLC (n = 256), UC (n = 240), melanoma (n = 200), RCC (n = 83), Ovarian cancer (n = 71), MCC (n = 78), SCLC (n = 45), HCC (n = 43), HNSCC (n = 39), Esophageal and GEJ cancer (n = 42), CRCa (n = 27), cervical cancer (n = 13), endometrial cancer (n = 12), CSCC (n = 11) and TNBC (n = 9) [93] (including review of supplementary appendix). The study only included checkpoint inhibitor monotherapy evaluation, and TMB for each tumor types was obtained and validated by NGS provided by Foundation Medicine. The study reported a significant correlation between the TMB and the ORR (P < 0.001). The correlation coefficient was 0.74 thus suggesting that 55% of the differences in the ORR across cancer types were explained by levels of TMB, with higher TMB correlating with better ORR (linear correlation).

A large chart review of patients who had undergone NGS by FoundationOne® assay and received checkpoint inhibitor therapy (anti-PD1, anti-PD-L1 and anti-CTLA4), revealed that higher TMB (defined as ≥20) was independently associated with better outcomes with response rate (RR) of 58% (vs. 20% for low-intermediate; P < 0.0001), mPFS of 12.8 months (vs. 3.3 months; P < 0.0001) and mOS not reached (vs. 16.3 months (P < 0.0036) [92]. Further sub-analysis of response in patients who received anti-PD1/PD-L1 monotherapy revealed a linear correlation between higher TMB and favorable outcome parameters with a median TMB for responders of 18.0 (vs. 5.0 for non-responders; P < 0.0001) [92]. Subjects of this study included those with NSCLC (n = 36), melanoma (n = 52), bladder cancer (n = 4), TNBC (n = 3), RCC (n = 6), CRCa (n = 5), CSCC (n = 8), HCC (n = 3), HNSCC (n = 13), MCC (n = 2) and ovarian cancer (n = 2).

A study utilizing data derived from the multipart phase 3 CheckMate 227 trial (NCT02477826) involved patients with stage 4 or recurrent NSCLC (n = 1004) who have not received prior chemotherapy [94]. The study investigated the therapeutic effect and safety of nivolumab/ipilimumab combination vs. nivolumab monotherapy vs. chemotherapy (reported as 1 year PFS, mPFS, ORR and rates of grades 3–4 treatment adverse events) across patient with low vs. high TMB levels. Those were obtained by FoundationOne® NGS assay, with a cut-off of 10 mut/Mb. PFS among patients with a high TMB was significantly longer with nivolumab/ipilimumab combination than with chemotherapy (1-year PFS: 42.6% vs. 13.2%; mPFS: 7.2 months vs. 5.5 months; HR for disease progression or death: 0.58; P < 0.001). Similarly, the ORR was 45.3% with nivolumab/ipilimumab combinations vs. 26.9% with chemotherapy. Interestingly, these results were consistent irrespective of PD-L1 expression levels (<1% or ≥1%) or histology (squamous vs. non-squamous types), thus providing one of the first pieces of evidence for the potential role for TMB as a biomarker of response to immune checkpoint therapy, that is more comprehensive than the current markers. The rate of grade 3 or 4 TAEs was lower in immune therapy combination (31.2%) compared to chemotherapy (36.1%) [94]. It is worth noting that patients who received nivolumab (PD-L1 only ≥1%) did not exhibit a significant difference in PFS compared to the chemotherapy group when stratified by TMB. This observation may highlight the need for dual immune therapy in patients with high TMB, as a reflection of higher immunogenicity whereby monotherapy alone may not be effective.

In a similar trial (CheckMate 032; NCT01928394) involving patients with SCLC who have received at least 1 platinum-containing chemotherapy regimen (n = 211), WES was used to evaluate the impact of TMB on efficacy of nivolumab monotherapy or in combination with ipilimumab [95]. TMB > 243 by WES was used as a cut-off (previously described in [96]). In both treatment groups, patients with high TMB had significantly higher (i) 1-year PFS rates (21.2% and 30.0% for nivolumab and nivolumab/ipilimumab, respectively compared to low: not calculable and 6.2%, respectively or medium TMB: 3.1% and 8.0 %, respectively), (ii) 1-year OS rates (35.2% and 62.4% for nivolumab monotherapy and nivolumab/ipilimumab combination, respectively compared to the low: 22.1% and 23.4%, respectively, or medium TMB: 26.0% and 19.6%, respectively), and (iii) ORR (21.3% and 46.2%, respectively, compared to low: 4.8% and 22.2%, respectively or medium TMB: 6.8% and 16.0%, respectively). Furthermore, among patients with a complete response (CR) or partial response (PR) to either monotherapy or combination therapy, TMB was significantly higher than in those with stable disease (SD) or progressive disease (PD). While such positive correlation between high TMB and higher therapeutic efficacy (PFS, PS, and ORR) was observed in monotherapy and combinational therapy (unlike the previous NSCLC study), 1-year PFS and 1-year OS rates were higher with nivolumab/ipilumumab combination compared to the nivolumab monotherapy. This enhanced efficacy of the combinational approach was further appreciated when using high tertile as a cut-off to define high TMB, again illustrating a potential role for TMB, not only as a biomarker of response to immune therapy, but as a guide to direct treatment regimen (mono vs. combinational approach).

A study by Forbe et al., assessed the effect of TMB in patients with stage I, II, or IIIA resectable NSCLC (n = 22) treated with neo-adjuvant nivolumab therapy [97]. TMB was reported as the number of somatic sequence alteration (estimated using the VariantDx software). Of the 20 tumors that were completely resected, major pathological response (PR + CR) occurred in 9/20 (45%) with a significant correlation between the pathological response and the pre-treatment TMB, in both PD-L1 positive and negative tumors.

In a cohort of 76 patients with advanced chemo-refractory HNSCC, gastric and esophageal cancer, response (ORR and disease control rate- DCR) to anti-PD1 therapy vs. anti-PD1 + chemotherapy was evaluated and compared between low and high TMB status (defined as ≥ 12 using FoundationOne® NGS) [98]. In the cohort receiving anti-PD1 monotherapy, the TMB high group showed significant superior OS compared to TMB low (14.6 vs.4.0 months; HR = 0.48; P < 0.05), while PD-L1 over-expression did not correlate with significant survival benefit.

Lastly, a recent meta-analysis conducted by Zhu et al., investigated the association between TMB and outcomes of cancer patients treated with PD-1/PD-L1 inhibitors across 2661 patients with advanced diverse solid malignancies [99]. The study adopted a cut-off of 10 mut/Mb to define high TMB, and inhibitors included nivolumab, pembrolizumab, atezolizumab, durvalumab and avemulab. Patients with current indications for immune checkpoint therapy included NSCLC and UC (n = 2510) and of them, those with high TMB exhibited significantly prolonged PFS compared to low, with HR ranging between 0.19 and 0.64 (95% CI ranging between 0.08 and 0.94) thus highlighting the significant benefits of PD-1/PD-L1 inhibition in patients with high TMB compared to those with low TMB.

The above gathered evidence strongly support a potential role for TMB as a preferable biomarker to screen and possibly direct therapy for the most appropriate patients who can benefit from PD-1/PD-L1 inhibition. Evidence also exists for the use of TMB as a biomarker of response to checkpoint inhibitors in patient populations with no current indication for immune blockade therapy. We will review those in the next section.

High TMB and response to checkpoint inhibitors therapy, outside the currently indicated populations

Data that positively correlates high TMB status and response to checkpoint inhibitors has also been reported in patients with tumors for which checkpoint blockage therapy is not indicated, either through large studies and data set reviews, or through case reports. In a large meta-analysis study which assessed the correlation between diverse anti-PD1 or anti-PD-L1 therapies, the cohort included patients with: adrenocortical cancer (n = 2), anal cancer (n = 3), germ cell tumor (n = 16), gliobastoma multiform (GBM; n = 35), mesothelioma (n = 57), pancreatic cancer (n = 36), prostate cancer (n = 77), and sarcoma (n = 87) [93]. The study reported a significant positive correlation between high TMB and ORR (P < 0.001).The study by Goodman et al., that was described earlier which compared outcomes from anti-PD1, anti-PD-L1 and anti-CTLA4 therapies in patients with high TMB (defined as ≥20 by FoundationOne® NGS assay) vs. low-intermediate TMB, also included patients outside current indications for checkpoint inhibitors [92]. These included: adrenal cancer (n = 1), appendiceal adenocarcinoma (n = 1), cervical cancer (n = 2), sarcoma (n = 3), basal cell carcinoma (BCC; n = 2), mesothelioma (n = 1), prostate cancer (n = 1), thyroid cancer (n = 3) and urethral carcinoma (n = 1). While overall data revealed that higher TMB independently associated with better outcomes as described above (RR 22/38; 58% P < 0.0001; mPFS 12.8 months P < 0.0001; and mOS not reached P < 0.0036), analysis of the available supplementary data revealed a similar association within the subgroup of patients without indications for immune checkpoint therapy. Out of the 22 patients with high TMB that exhibited PR/CR, prolonged mPFS and mOS, 8 patients received immune checkpoint blockade outside indication, including BCC (n = 2/2), sarcoma (n = 2/3), cervical (n = 2/2), urethral (n = 1/1) and prostate (n = 1/1) cancers. Similarly, within the population that excluded NSCLC and melanoma patients, analysis revealed a RR of 47% in the high TMB group (vs. 9% in low-intermediate; P = 0.006), and mPFS of 10.0 months (vs. 2.1 months; P = 0.003).

Similar observations have been reported in single patients as part of published case reports. In a study published by Ikeda et al., 2016, a 58 year old man with PDL1 + metastatic BCC (to the liver) and a high TMB of 79 Mut/Mb (by NGS) who had failed 4 prior lines of therapy, was treated with nivolumab. The patient achieved near CR of the hepatic lesion, along with significant improvement in fatigue, mood and appetite [100].

Similarly, a patient with stage 1b endometrial adenocarcinoma (previously failed 3 lines of therapy) and high TMB status (4500–6500 mutations by WES), was treated with pembrolizumab [101]. The patient achieved PR at 8 weeks and a durable response for over 14 months (persisted at the time the case was published), as well as symptomatic improvement.

In a patient with metastatic MBD4-germline mutated uveal melanoma refractory to prior lines of therapy and a TMB five-fold higher than average TMB, treatment with ipilimumab and pembrolizumab combination resulted in a positive clinical response [102]: the patient experienced SD for 10 months and survived for 2 years with metastatic disease, a duration twice as long as median survival.

In a study by Saller et al., a patient with advanced classic Kaposi sarcoma refractory to chemotherapy was successfully treated with pembrolizumab. The patient had intermediate TMB (7 mut/Mb) and achieved a PR with significant reduction in tumor burden up to 42 weeks of therapy [103].

A 53-year woman with intra-hepatic cholangio-carcinoma refractory to surgical and chemotherapy was reported to have been successfully treated with pembrolizumab [104]. The patient had high PD-L1 expression (TPS 80%) and a high TMB (19.3Mut/Mb), and was administered six cycles of pembrolizumab + chemotherapy, followed by pembrolizumab alone and achieved CR for 10 months before being lost to follow up.

Interestingly, cases of response to checkpoint inhibitor in patients with high TMB but negative PD-L1 status, other than the one we previously described, have also been reported: a 64-year-old patient with PD-L1 negative (<1%) large cell neuroendocrine carcinoma refractory to chemotherapy and radiation therapy, and metastatic to the pancreas and bone, was treated with pembrolizumab after he was found to have a high TMB (24.76 Mut/Mb by FoundationOne® assay NGS) [105]. Within one cycle of therapy, all visible lesions shrunk and no new lesions were seen. The patient maintained a PR at 6 months and at the time the case was published. A similar case of a PD-L1 negative tumor but high TMB has also been reported [106]: a 48-year-old woman with metastatic, chemotherapy-refractory neuroendocrine carcinoma of the cervix was found to carry a PD-L1 negative, high-TMB (53 Mut/Mb) genomic profile by NGS (FoundationOne®). Patient was treated with SBRT and nivolumab and exhibited a remarkable local and systemic response with a near-complete tumor resolution over than 10 months, and ongoing at the time of publication.

TMB and BRCA1/2 correlation in breast, and ovarian cancer

While ICIs have robust responses in mismatch deficient tumors, there is a mixed response in tumors harboring mutations in DNA damage response and homologous recombination pathways (HRD) [107, 108]. Included are somatic or germline BRCA1/2 mutations. These tumors were originally hypothesized to be highly immunogenic due to defects in these pathways, which drive genomic instability which should produce more neoantigens as a result of single nucleotide (SNV) and copy number variation (CNV). While BRCA1 mutant breast tumors are highly aneuploid, somatic copy number variations (SCNA) have been shown to negatively correlate with ICI response [109]. The exact mechanism for this is unclear, but one hypothesis is that aneuploidy occurs early in cancer development and mutations accumulate secondarily to drive cancer progression and metastasis [109]. This is likely the case in germ lineage BRCA1/2 mutations, in which there is only a slight increase in TMB and neoantigen presentation compared to the degree of aneuploidy present [110]. Alternatively, immunity is a tightly regulated process in which the relative ratios of proteins needed is in delicate balance which may be impaired by CNV [109]. One study evaluated BRCA1/2 deficient tumors and stratified results based on HRD status; low (no other mutations in homologous recombination pathways) or high (concurrent mutation in another homologous recombination gene) and HR status (HR+ or TNBC). They found that somatic or germline BRCA1/2 mutant, HRD-low (homologous recombination deficient) breast cancers are the most immunogenic subset, which was independent of TMB [110]. This subset also had increased TGFβ gene expression which may recruit increased amounts of myofibroblasts to the tumor microenvironment [110]. Additionally, results showed HRD high, hormone receptor positive tumors had the least amount of CD8 T cell response, cytolytic index, PD-L1 expression and immune infiltrates. These results indicate that tumor intrinsic factors play a more important role in driving immune response. The use of HR status and HRD could be a novel biomarker to predict response to checkpoint inhibitors irrespective of TMB.

BRCA1 mutant breast cancers have also been linked to a T cell-inflamed gene signature (TIS), which analyzes 18 genes involved in the adaptive immune response [111]. Included in this gene expression profile (GEP) are genes involved in interferon activity, T cell abundance, exhaustion and antigen-presenting cell abundance. This signature has been used to successfully predict response to pembrolizumab in various cancer types [112]. The distinction between T cell abundance and exhaustion is crucial to determine which tumors will have the strongest CD8 T cell response.

Similarly, in high grade serous ovarian cancer (HGSOC), BRCA1 mutation has been proposed as a biomarker for ICI response, regardless of TMB [113]. HRD HGSOC harbors an increased mutational status and increased TILs compared to homologous recombination proficient tumors [114]. However, there has been little investigation into whether or not this correlates with responsiveness to ICI. One clinical trial has evaluated the efficacy of PARP inhibitors in combination with the checkpoint inhibitor avelumab. The JAVELIN Ovarian PARP 100 study which randomized patients to three arms; chemotherapy and avelumab followed by maintenance avelumab plus talazoparib, chemotherapy followed by talazoparib maintenance or chemotherapy plus bevacizumab followed by maintenance bevacizumab (NCT03642132). This trial was stopped early due to several factors including results from the JAVELIN 100 Ovarian cancer study. In that study, avelumab was evaluated in front line therapy in combination with chemotherapy, chemotherapy plus maintenance avelumab or chemotherapy alone. This study failed to meet its primary endpoint and was discontinued early. It is important to note that patients were not enrolled based on prospective biomarker status which could have greatly influenced the results. Retrospective, biomarker analysis completed in the phase Ib JAVELIN solid tumor trial, indicated that BRCA 1/2 mutation status did not predict response, however results could need to be stratified based on HRD status as discussed above [110, 115]. Additionally, the sample size in this study was limited (n = 8), which could have an effect on the data.

In the JAVELIN Ovarian 200 study, avelumab with or without pegylated doxyrubicin (PLD) in advanced stage platinum-resistant or refractory ovarian cancer was evaluated (NCT02580058). Results suggested that the combination of avelumab + PLD led to better overall response rate (ORR) (13.3%, 95% CI 8.8–19.0) compared to either avelumab (3.7, 95% CI 1.5–7.5) or PLD alone (4.2%, 95% CI 1.8–8.1). In the avelumab + PLD arm, these data were further distinguished when PD-L1 status was considered. In avelumab + PLD patients with a PD-L1 status >1% (N = 92), the ORR increased to 18.5% (95% CI 11.1–27.9), compared to PD-L1 < 1% patients (N = 58), whose ORR was 3.4% (95% CI 0.4–11.9). Moreover, OS was 18.4 months in PD-L1 > 1% compared to 12.7 months in the PD-L1 ≤ 1% group who received avelumab and PLD. This suggests that PD-L1 status has an important role in determination of responsiveness to avelumab therapy. Overall survival (OS) was not significantly improved with Avelumab alone (11.8 months, 95% CI 8.9–14.1) or Avelumab + PLD (15.7 months, 95% CI 12.7–18.7) compared to PLD alone (13.1 months, 95% CI 11.8–15.5). Median progression free survival (PFS) also did not differ significantly between avelumab + PLD (3.7, 95% CI 3.3–5.1) and PLD alone (3.5, 95% CI 2.1–4.0). But, in the PD-L1 > 1% group, OS and PFS improved in the avelumab + PLD group (OS = 18.4, 95% CI 13.7-22.0; PFS = 3.7, 95% CI 2.2–5.6) compared to the PLD group (OS = 13.8, 95% CI 10.5–17.7; PFS = 1.9, 95% CI 1.9–3.6) [116]. These data as presented do not indicate significantly improved outcomes compared to standard therapy. However, the fact that PD-L1 + expression correlated with good response in phase Ib and phase III trials of avelumab, suggests that a sensitive subset population in ovarian cancer may exist. This is corroborated by the improved ORR, OS, and PFS described in PD-L1 > 1% groups receiving avelumab + PLD in the JAVELIN Ovarian 200 trial described above. This evidence indicates that there is a subset of patients that will respond to therapy and highlights the urgent need to identify a biomarker specific to response. Combination of numerous factors (PD-L1, TMB, HRD or BRCA mutation status) may be needed to more accurately predict response in ovarian cancer [117].

Future directions

We have presented evidence that TMB is an important clinical biomarker that may predict response to ICI, we also discussed the use of PD-L1, and the benefits and limitations to predict response to ICI and T-cell-inflamed gene signature and it’s use to predict response to pembrolizumab. However, there are currently a number of other factors under investigation not yet clinically validated that may add to our armamentarium in the future for response prediction. Here we will discuss, those factors, including neoantigen heterogeneity, histone modification, DNA methylation and role of gut microbiome.

Tumor heterogeneity relates to a biomarker ICI responsiveness. Tumors accumulate mutations in a step-wise fashion, therefore mutations that occur early on in the process, become clonal and are expressed in higher numbers through out the tumor. This is important from a clinical response perspective as neoantigens that are highly expressed are recognized by the immune system more effectively. In a retrospective study of NCSLC patients treated with pembrolizumab, PFS was significantly longer when tumors had a high clonal neoantigen burden and low intratumor heterogeneity (ITH) [2]. Tumors with high subclonal neoantigens and low clonal neoantigens did not respond as well to pembrolizumab. We also show that T cells were only effective in identifying clonal neoantigens and were unable to recognize subclonal neoantigens. This data indicates that the amount of clonal neoantigens present in the tumor likely plays a key role in T cell recognition and response. The inability of T cells to recognize subclonal neoantigens has a significant impact on anti-cancer immune response. This supports interest in personalized vaccines, which would educate the immune system on the individual clonal neoantigen repertoire present in the tumor.

One way researchers have found to increase neoantigen targeting CD8 T cell is through the gut microbiota. In murine models, a mix of 11 bacterial strains commonly found in the human gut elicited expression of Cxcl9 and Cxcl10 and IFNγ+ CD8 T cells. Mice engrafted subcutaneously with MC38 colon cancer cells, had a significantly better response to anti-PD-1, or anti-CTLA-4 therapy when pretreated with the 11 mix of bacteria, which indicates that gut microbiota is able to elicit CD8 T cell dependent immunity [118]. Another group found that only Bacteriodes strains were needed to sensitize germ free mice with MCA205 fibrosarcomas, Ret melanoma and MC38 colon cancer models to CTLA-4 blockade [119]. Interestingly, this strain was present in a wider selection of microbes from the Tanoue et al., mix of bacteria but was not needed to elicit CD8 T cells and increase responses to checkpoint blockade. While this remains an attractive avenue to pursue in order to increase immune checkpoint inhibition response, there have been no studies in humans to determine the efficacy as either a biomarker of responsiveness to ICI or as a prebiotic to ICI therapy. In fact, fecal material transplant (FMT) is not FDA approved and clinical trials were suspended in June, 2019 after one patient died and another became ill following FMT to treat Clostridium difficile infection.

Another possible mechanism to increase the CD8 T cell populations, is to reduce the proportion of exhausted T cells with in the TME and increase PD-L1 expression. To accomplish this, it has been proposed that de novo DNA methylation plays a critical role in maintaining the exhausted phenotype in viral infection even with out the presence of antigen [120, 121]. Ghoneim et al., were able to show that not only does de novo DNA methylation drive T cell differentiation and exhaustion, but that this persists even after PD-1 inhibition in chronic viral models of infection [122]. In murine tumor models, the methylation of Tcf7, Ccr7, Myc and IFNγ were compared between tumor matched groups, PD-1hi, Tim3+, CD8+ and PD-1lo, CD8+ TILS. All four gene loci were methylated in the PD-1hi group where as only IFNγ and Myc were methylated in the PD-1lo group, indicating that de novo DNA methylation plays a role in perpetuating T cell exhaustion. When tumor bearing mice were treated with decitabine (DEC), a demethylating agent, followed by PD-L1 blockade, there was an increase in CD8 T cells and decreased tumor growth. Additionally, inhibiting DNA methylation in melanoma cell lines, results in increased PD-L1 expression [123].

Histone modification has recently been shown to regulate immune cells, including T cells [124]. Histone modifications, including methylation and acetylation work to either create space between histones and DNA, or to condense the structure, respectively. Both modifications have been shown to promote tumorigenesis [125, 126]. In preclinical models of NSCLC, combination treatment of mocetinostat and PD-L1 antibody increased anti-tumor efficacy compared to either treatment alone [127]. In models of HCC, combination of belinostat with CTLA-4 resulted in increased efficacy and a triple combination with PD-1 inhibitor resulted in 100% survival of mice [128].

Other determinants of immunotherapy response have also been well described by Conway et al. [129]. Specifically, they outlined how alterations in canonical cancer pathways (MAPK, PI3K, WNT-β-catenin) is related to immunotherapy resistance. How PTEN loss is associated with reduction in tumor infiltrating lymphocyte migration. The role of IDO1 in T cell and NK suppression within the tumor microenvironment and relationship of DNA repair deficiency to neoantigen extension. Loss of function in the JAK-STAT pathway and role of TGFβ were also reviewed for immune suppressive role. Biomarker opportunity of each of these immune modulating components will require further clinical assessment but are additional promising directions.

While there is a growing body of literature examining ways to sensitize a patient, or enhance the response to ICI, this work has been done with retrospective analysis or is still in preclinical stages. More work will need to be done to determine if any of these methods will transfer to the clinic.


TMB is an emerging biomarker of sensitivity to immune checkpoint inhibitors with many studies showing a more significant association with response to PD-1, PD-L1, and CTLA-4 blockage immune therapy, than PD-1 or PD-L1 expression, as measured by IHC. More importantly there is shortage of evidence supporting this significant correlation whether within, or outside current indications for immune checkpoint therapy. Whether through analysis of large trial data, or through meta-analyses, observational studies and case reports, patients with high TMB status treated with immune checkpoint blockade therapy have shown prolonged PFS and OS compared to those with low or intermediate TMB, and most importantly irrespective of PD-L1 status. Many other retrospective and observational studies have also explored the relationship between TMB and other tumor markers such as T cell-inflamed GEP for example refs. [5, 130]. Similarly, other studies have explored TMB as a reflection of high rates of mutations affecting DNA repair genes, such as: ATM, MRCA2, MSH6, MLH1, LIG1, POLE, BRCA1, MSH2, SLX4, FANCM, and FANCD2 [101, 131,132,133]. Positive response to immune blockade therapy in these “ultra-mutator” phenotypes irrespective of PD-L1 expression levels, further support the role of high TMB as a promising marker with higher sensitivity compared to currently used PD-L1.

Although there are currently no clearly set cut-off to define high vs. low TMB, there seems to be a common consensus to use a TMB ≥ 10 Mut/Mb by FoundationOne® NGS assay or TMB ≥ 243 by WES assay. Most importantly, and regardless of the set cut-off the correlation between high TMB and positive response to immune checkpoint blockage remains positive and TMB represents an effective and important biomarker to predict responsiveness to immune checkpoint inhibitors, irrespective of tumor type, and in a more inclusive and comprehensive fashion that current PD-L1 marker.

Despite that, no prospective study has yet established the utility of TMB in selecting and directing immune therapy. In light of the emergence of basket trial designs that evaluate potential biomarkers of response to targeted therapy independent of tumor histology [134], we have here gathered significant data in support of conducting such trials in patients with either PD-L1 positive (≥1%) or negative tumors.


  1. 1.

    Brown SD, Warren RL, Gibb EA, Martin SD, Spinelli JJ, Nelson BH, et al. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res. 2014;24(Suppl 5):743–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016;351(Suppl 6280):1463–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015;348(Suppl 6230):69–74.

    CAS  PubMed  Google Scholar 

  4. 4.

    Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017;9(Suppl 1):34.

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Cristescu R, Mogg R, Ayers M, Albright A, Murphy E, Yearley J, et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy. Science. 2018;362(Suppl 6411):eaar3593.

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Johnson DB, Frampton GM, Rioth MJ, Yusko E, Xu Y, Guo X, et al. Targeted next generation sequencing identifies markers of response to PD-1 blockade. Cancer Immunol Res. 2016;4(Suppl 11):959–67.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Samstein RM, Lee C-H, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51(Suppl 2):202–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl J Med. 2014;371(Suppl 23):2189–99.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Panda A, Betigeri A, Subramanian K, Ross JS, Pavlick DC, Ali S, et al. Identifying a clinically applicable mutational burden threshold as a potential biomarker of response to immune checkpoint therapy in solid tumors. JCO Precis Oncol. 2017;1:1–13.

    Google Scholar 

  10. 10.

    Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458(Suppl 7239):719.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Chabanon RM, Pedrero M, Lefebvre C, Marabelle A, Soria J-C, Postel-Vinay S. Mutational landscape and sensitivity to immune checkpoint blockers. Clin Cancer Res. 2016;22(Suppl 17):4309–21.

    CAS  PubMed  Google Scholar 

  12. 12.

    Chen Y-P, Zhang Y, Lv J-W, Li Y-Q, Wang Y-Q, He Q-M, et al. Genomic analysis of tumor microenvironment immune types across 14 solid cancer types: immunotherapeutic implications. Theranostics. 2017;7(Suppl 14):3585.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Liontos M, Anastasiou I, Bamias A, Dimopoulos M-A. DNA damage, tumor mutational load and their impact on immune responses against cancer. Ann. Transl. Med. 2016;4(Suppl 14):264.

  14. 14.

    Campbell BB, Light N, Fabrizio D, Zatzman M, Fuligni F, de Borja R, et al. Comprehensive analysis of hypermutation in human cancer. Cell. 2017;171(Suppl 5):1042–56.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(Suppl 6):703.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Greaves M, Maley CC. Clonal evolution in cancer. Nature. 2012;481(Suppl 7381):306.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature. 2013;500(Suppl 7463):415.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31(Suppl 11):1023.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Singh RR, Patel KP, Routbort MJ, Reddy NG, Barkoh BA, Handal B, et al. Clinical validation of a next-generation sequencing screen for mutational hotspots in 46 cancer-related genes. J Mol diagnostics. 2013;15(Suppl 5):607–22.

    CAS  Google Scholar 

  20. 20.

    Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461(Suppl 7261):272.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Warr A, Robert C, Hume D, Archibald A, Deeb N, Watson M. Exome sequencing: current and future perspectives. G3: Genes, Genomes, Genet. 2015;5(Suppl 8):1543–50.

    Google Scholar 

  22. 22.

    Baras AS, Stricker T. Characterization of total mutational burden in the GENIE cohort: Small and large panels can provide TMB information but to varying degrees. Cancer Res. 2017;77(Suppl 13):LB-105.

  23. 23.

    Gong J, Pan K, Fakih M, Pal S, Salgia R. Value-based genomics. Oncotarget. 2018;9(Suppl 21):15792.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Hargadon KM, Johnson CE, Williams CJ. Immune checkpoint blockade therapy for cancer: an overview of FDA-approved immune checkpoint inhibitors. Int Immunopharmacol. 2018;62:29–39.

    CAS  PubMed  Google Scholar 

  25. 25.

    D.I.S.C.O F. FDA grants accelerated approval to pembrolizumab for first tissue/site agnostic indication. In: U.S. Food and Drug Administration. 2017.

  26. 26.

    Company B-MS. Prescribing information ipilimumab (Yervoy®). Princeton; 2018.

  27. 27.

    MERCK & CO. I. Prescribing Information Pembrolizumab (Keytruda®). Whitehouse Station; 2019.

  28. 28.

    Squibb B-M. Prescribing Information Nivolumab (Opdivo®). Princeton; 2019.

  29. 29.

    Regeneron Pharmaceuticals I. Prescribing information cemiplimab (Libtayo®). In: LCC S-AUS. NJ; 2019.

  30. 30.

    Genetech I. Prescribing information Atezolizumab (Tecentriq®). South San Francisco; 2019.

  31. 31.

    EMD Serono I. Prescribing information avelumab (Bavencio®). Rockland; 2019.

  32. 32.

    Pharmaceuticals A. Prescribing information durvalumab (Imfinzi®). Wilmington; 2018.

  33. 33.

    Seidel JA, Otsuka A, Kabashima K. Anti-PD-1 and anti-CTLA-4 therapies in cancer: mechanisms of action, efficacy, and limitations. Front Oncol. 2018;8:86.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Parry RV, Chemnitz JM, Frauwirth KA, Lanfranco AR, Braunstein I, Kobayashi SV, et al. CTLA-4 and PD-1 receptors inhibit T-cell activation by distinct mechanisms. Mol Cell Biol. 2005;25(Supl 21):9543–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Jin H-T, Ahmed R, Okazaki T. Role of PD-1 in regulating T-cell immunity. In: Negative Co-Receptors and Ligands. Springer;2010. p. 17–37.

  36. 36.

    Jenkins RW, Barbie DA, Flaherty KT. Mechanisms of resistance to immune checkpoint inhibitors. Br J cancer. 2018;118(Suppl 1):9.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Marzec M, Zhang Q, Goradia A, Raghunath PN, Liu X, Paessler M, et al. Oncogenic kinase NPM/ALK induces through STAT3 expression of immunosuppressive protein CD274 (PD-L1, B7-H1). Proc Natl Acad Sci USA. 2008;105(Suppl 52):20852–7.

    CAS  PubMed  Google Scholar 

  38. 38.

    Parsa AT, Waldron JS, Panner A, Crane CA, Parney IF, Barry JJ, et al. Loss of tumor suppressor PTEN function increases B7-H1 expression and immunoresistance in glioma. Nat Med. 2007;13(Suppl 1):84.

    CAS  PubMed  Google Scholar 

  39. 39.

    Ribas A. Adaptive immune resistance: how cancer protects from immune attack. Cancer Discov. 2015;5(Suppl 9):915–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(Suppl 4):252.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    O’Donnell JS, Long GV, Scolyer RA, Teng MWL, Smyth MJ. Resistance to PD1/PDL1 checkpoint inhibition. Cancer Treat Rev. 2017;52:71–81.

    PubMed  Google Scholar 

  42. 42.

    Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. 2017;168(Suppl 4):707–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Ribas A, Shin DS, Zaretsky J, Frederiksen J, Cornish A, Avramis E, et al. PD-1 blockade expands intratumoral memory T cells. Cancer Immunol Res. 2016;4(Suppl 3):194–203.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Farber DL, Yudanin NA, Restifo NP. Human memory T cells: generation, compartmentalization and homeostasis. Nat Rev Immunol. 2014;14(Suppl 1):24.

    CAS  PubMed  Google Scholar 

  45. 45.

    Harty JT, Badovinac VP. Shaping and reshaping CD8+ T-cell memory. Nat Rev Immunol. 2008;8(Suppl 2):107.

    CAS  PubMed  Google Scholar 

  46. 46.

    Fares CM, Van Allen EM, Drake CG, Allison JP, Hu-Lieskovan S. Mechanisms of resistance to immune checkpoint blockade: why does checkpoint inhibitor immunotherapy not work for all patients? Am Soc Clin Oncol Educ Book. 2019;39:147–64.

    PubMed  Google Scholar 

  47. 47.

    Kreiter S, Vormehr M, Van de Roemer N, Diken M, Löwer M, Diekmann J, et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature. 2015;520(Suppl 7549):692.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science. 2015;348(Suppl 6230):124–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350(Suppl 6257):207–11.

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Martin AM, Nirschl TR, Nirschl CJ, Francica BJ, Kochel CM, van Bokhoven A, et al. Paucity of PD-L1 expression in prostate cancer: innate and adaptive immune resistance. Prostate cancer prostatic Dis. 2015;18(Suppl 4):325.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti–PD-1 antibody in cancer. N. Engl J Med. 2012;366(Suppl 26):2443–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Korkolopoulou P, Kaklamanis L, Pezzella F, Harris A, Gatter K. Loss of antigen-presenting molecules (MHC class I and TAP-1) in lung cancer. Br J cancer. 1996;73(Suppl 2):148.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Zhao F, Sucker A, Horn S, Heeke C, Bielefeld N, Schrörs B, et al. Melanoma lesions independently acquire T-cell resistance during metastatic latency. Cancer Res. 2016;76(Suppl 15):4347–58.

    CAS  PubMed  Google Scholar 

  54. 54.

    Keenan TE, Burke KP, Van Allen EM. Genomic correlates of response to immune checkpoint blockade. Nat Med. 2019;25:389–4021.

  55. 55.

    Peng W, Chen JQ, Liu C, Malu S, Creasy C, Tetzlaff MT, et al. Loss of PTEN promotes resistance to T cell–mediated immunotherapy. Cancer Discov. 2016;6(Suppl 2):202–16.

    CAS  PubMed  Google Scholar 

  56. 56.

    George S, Miao D, Demetri GD, Adeegbe D, Rodig SJ, Shukla S, et al. Loss of PTEN is associated with resistance to anti-PD-1 checkpoint blockade therapy in metastatic uterine leiomyosarcoma. Immunity. 2017;46(Suppl 2):197–204.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature. 2015;523(Suppl 7559):231.

    CAS  PubMed  Google Scholar 

  58. 58.

    Koyama S, Akbay EA, Li YY, Aref AR, Skoulidis F, Herter-Sprie GS, et al. STK11/LKB1 deficiency promotes neutrophil recruitment and proinflammatory cytokine production to suppress T-cell activity in the lung tumor microenvironment. Cancer Res. 2016;76(Suppl 5):999–1008.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Sivan A, Corrales L, Hubert N, Williams JB, Aquino-Michaels K, Earley ZM, et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti–PD-L1 efficacy. Science. 2015;350(Suppl 6264):1084–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Vétizou M, Pitt JM, Daillère R, Lepage P, Waldschmitt N, Flament C, et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science. 2015;350(Suppl 6264):1079–84.

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Ellis LM, Hicklin DJ. VEGF-targeted therapy: mechanisms of anti-tumour activity. Nat Rev cancer. 2008;8(Suppl 8):579.

    CAS  PubMed  Google Scholar 

  62. 62.

    Young MRI, Wright MA, Coogan M, Young ME, Bagash J. Tumor-derived cytokines induce bone marrow suppressor cells that mediate immunosuppression through transforming growth factor β. Cancer Immunol, Immunother. 1992;35(Suppl 1):14–8.

    CAS  Google Scholar 

  63. 63.

    Commeren DL, Van Soest PL, Karimi K, Löwenberg B, Cornelissen JJ, Braakman E. Paradoxical effects of interleukin‐10 on the maturation of murine myeloid dendritic cells. Immunology. 2003;110(Suppl 2):188–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Pitt JM, Vétizou M, Daillère R, Roberti MP, Yamazaki T, Routy B, et al. Resistance mechanisms to immune-checkpoint blockade in cancer: tumor-intrinsic and-extrinsic factors. Immunity. 2016;44(Suppl 6):1255–69.

    CAS  PubMed  Google Scholar 

  65. 65.

    Keir ME, Butte MJ, Freeman GJ, Sharpe AH. PD-1 and its ligands in tolerance and immunity. Annu Rev Immunol. 2008;26:677–704.

    CAS  PubMed  Google Scholar 

  66. 66.

    Rudensky AY. Regulatory T cells and Foxp3. Immunological Rev. 2011;241(Suppl 1):260–8.

    CAS  Google Scholar 

  67. 67.

    Chaudhary B, Elkord E. Regulatory T cells in the tumor microenvironment and cancer progression: role and therapeutic targeting. Vaccines. 2016;4(Suppl 3):28.

    PubMed Central  Google Scholar 

  68. 68.

    Chanmee T, Ontong P, Konno K, Itano N. Tumor-associated macrophages as major players in the tumor microenvironment. Cancers. 2014;6(Suppl 3):1670–90.

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Highfill SL, Cui Y, Giles AJ, Smith JP, Zhang H, Morse E, et al. Disruption of CXCR2-mediated MDSC tumor trafficking enhances anti-PD1 efficacy. Sci Transl Med. 2014;6(Suppl 237):237ra67–237ra67.

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Gil M, Komorowski MP, Seshadri M, Rokita H, McGray AR, Opyrchal M, et al. CXCL12/CXCR4 blockade by oncolytic virotherapy inhibits ovarian cancer growth by decreasing immunosuppression and targeting cancer-initiating cells. J Immunol. 2014;193(Suppl 10):5327–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N. Engl J Med. 2015;373(Suppl 1):23–34.

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Ferris RL, Blumenschein G Jr, Fayette J, Guigay J, Colevas AD, Licitra L, et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N. Engl J Med. 2016;375(Suppl 19):1856–67.

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, et al. Nivolumab versus docetaxel in advanced nonsquamous non–small-cell lung cancer. N. Engl J Med. 2015;373(Suppl 17):1627–39.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Brahmer J, Reckamp KL, Baas P, Crinò L, Eberhardt WE, Poddubskaya E, et al. Nivolumab versus docetaxel in advanced squamous-cell non–small-cell lung cancer. N. Engl J Med. 2015;373(Suppl 2):123–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Nghiem PT, Bhatia S, Lipson EJ, Kudchadkar RR, Miller NJ, Annamalai L, et al. PD-1 blockade with pembrolizumab in advanced Merkel-cell carcinoma. N. Engl J Med. 2016;374(Suppl 26):2542–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, et al. Pembrolizumab for the treatment of non–small-cell lung cancer. N. Engl J Med. 2015;372(Suppl 21):2018–28.

    PubMed  Google Scholar 

  77. 77.

    Reck M, Rodríguez-Abreu D, Robinson AG, Hui R, Csőszi T, Fülöp A, et al. Pembrolizumab versus chemotherapy for PD-L1–positive non–small-cell lung cancer. N. Engl J Med. 2016;375(Suppl 19):1823–33.

    CAS  PubMed  Google Scholar 

  78. 78.

    Herbst RS, Baas P, Kim D-W, Felip E, Pérez-Gracia JL, Han J-Y, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet. 2016;387(Suppl 10027):1540–50.

    CAS  PubMed  Google Scholar 

  79. 79.

    Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl J Med. 2015;372(Suppl 26):2509–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Rittmeyer A, Barlesi F, Waterkamp D, Park K, Ciardiello F, Von Pawel J, et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet. 2017;389(Suppl 10066):255–65.

    PubMed  Google Scholar 

  81. 81.

    Balar AV, Galsky MD, Rosenberg JE, Powles T, Petrylak DP, Bellmunt J, et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet. 2017;389(Suppl 10064):67–76.

    CAS  PubMed  Google Scholar 

  82. 82.

    Robert C, Schachter J, Long GV, Arance A, Grob JJ, Mortier L, et al. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl J Med. 2015;372(Suppl 26):2521–32.

    CAS  PubMed  Google Scholar 

  83. 83.

    Robert C, Thomas L, Bondarenko I, O’Day S, Weber J, Garbe C, et al. Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N. Engl J Med. 2011;364(Suppl 26):2517–26.

    CAS  PubMed  Google Scholar 

  84. 84.

    Antonia SJ, López-Martin JA, Bendell J, Ott PA, Taylor M, Eder JP, et al. Nivolumab alone and nivolumab plus ipilimumab in recurrent small-cell lung cancer (CheckMate 032): a multicentre, open-label, phase 1/2 trial. Lancet Oncol. 2016;17(Suppl 7):883–95.

    CAS  PubMed  Google Scholar 

  85. 85.

    Topalian SL, Taube JM, Anders RA, Pardoll DM. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer. 2016;16(Suppl 5):275.

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Ilie M, Hofman V, Dietel M, Soria J-C, Hofman P. Assessment of the PD-L1 status by immunohistochemistry: challenges and perspectives for therapeutic strategies in lung cancer patients. Virchows Arch. 2016;468(Suppl 5):511–25.

    CAS  PubMed  Google Scholar 

  87. 87.

    Motzer RJ, Escudier B, McDermott DF, George S, Hammers HJ, Srinivas S, et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N. Engl J Med. 2015;373(Suppl 19):1803–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Horn L, Spigel DR, Vokes EE, Holgado E, Ready N, Steins M, et al. Nivolumab versus docetaxel in previously treated patients with advanced non–small-cell lung cancer: two-year outcomes from two randomized, open-label, phase III trials (CheckMate 017 and CheckMate 057). J Clin Oncol. 2017;35(Suppl 35):3924.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Bhaijee F, Anders RA. PD-L1 expression as a predictive biomarker: Is absence of proof the same as proof of absence? JAMA Oncol. 2016;2(Suppl 1):54–5.

    PubMed  Google Scholar 

  90. 90.

    Soria J-C, Marabelle A, Brahmer JR, Gettinger S. Immune checkpoint modulation for non–small cell lung cancer. AACR;2015.

  91. 91.

    Barve M, Adams N, Plato L, Dupler R, Anand R, Jones J, et al. Case Report: immune checkpoint inhibitor elicited complete response in a heavily pretreated patient with metastatic endometrial carcinoma with a high tumor mutation burden (TMB). Mol Med: Curr Asp. 2017;1(Suppl 1):005.

    Google Scholar 

  92. 92.

    Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM, Miller V, et al. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol cancer therapeutics. 2017;16(Suppl 11):2598–608.

    CAS  Google Scholar 

  93. 93.

    Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N. Engl J Med. 2017;377(Suppl 25):2500.

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Hellmann MD, Ciuleanu T-E, Pluzanski A, Lee JS, Otterson GA, Audigier-Valette C, et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N. Engl J Med. 2018;378(Suppl 22):2093–104.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Hellmann MD, Callahan MK, Awad MM, Calvo E, Ascierto PA, Atmaca A, et al. Tumor mutational burden and efficacy of nivolumab monotherapy and in combination with ipilimumab in small-cell lung cancer. Cancer cell. 2018;33(Suppl 5):853–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L, Steins M, et al. First-line nivolumab in stage IV or recurrent non–small-cell lung cancer. N. Engl J Med. 2017;376(Suppl 25):2415–26.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Forde PM, Chaft JE, Smith KN, Anagnostou V, Cottrell TR, Hellmann MD, et al. Neoadjuvant PD-1 blockade in resectable lung cancer. N. Engl J Med. 2018;378(Suppl 21):1976–86.

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Wang F, Wei X, Wang F, Xu N, Shen L, Dai G et al. Safety, efficacy and tumor mutational burden as a biomarker of overall survival benefit in chemo-refractory gastric cancer treated with toripalimab, a PD1 antibody in phase Ib/II clinical trial NCT02915432. Ann Oncol. 2019;30:1479–1486.

  99. 99.

    Zhu J, Zhang T, Li J, Lin J, Liang W, Huang W, et al. Association between Tumor Mutation Burden (TMB) and outcomes of cancer patients treated with PD-1/PD-L1 inhibitions: a meta-analysis. Front Pharmacol. 2019;10:673.

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Ikeda S, Goodman AM, Cohen PR, Jensen TJ, Ellison CK, Frampton G, et al. Metastatic basal cell carcinoma with amplification of PD-L1: exceptional response to anti-PD1 therapy. NPJ Genom Med. 2016;1:16037.

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    Mehnert JM, Panda A, Zhong H, Hirshfield K, Damare S, Lane K, et al. Immune activation and response to pembrolizumab in POLE-mutant endometrial cancer. J Clin Investig. 2016;126(Suppl 6):2334–40.

    PubMed  Google Scholar 

  102. 102.

    Johansson PA, Stark A, Palmer JM, Bigby K, Brooks K, Rolfe O, et al. Prolonged stable disease in a uveal melanoma patient with germline MBD4 nonsense mutation treated with pembrolizumab and ipilimumab. Immunogenetics. 2019;71(Suppl 5–6):433–6.

    CAS  PubMed  Google Scholar 

  103. 103.

    Saller J, Walko CM, Millis SZ, Henderson-Jackson E, Makanji R, Brohl AS. Response to checkpoint inhibitor therapy in advanced classic Kaposi sarcoma: a case report and immunogenomic study. J Natl Compr Cancer Netw. 2018;16(Suppl 7):797–800.

    Google Scholar 

  104. 104.

    Mou H, Yu L, Liao Q, Hou X, Wu Y, Cui Q, et al. Successful response to the combination of immunotherapy and chemotherapy in cholangiocarcinoma with high tumour mutational burden and PD-L1 expression: a case report. BMC cancer. 2018;18(Suppl 1):1105.

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Wang VE, Urisman A, Albacker L, Ali S, Miller V, Aggarwal R, et al. Checkpoint inhibitor is active against large cell neuroendocrine carcinoma with high tumor mutation burden. J Immunother cancer. 2017;5(Suppl 1):75.

    PubMed  PubMed Central  Google Scholar 

  106. 106.

    Sharabi A, Kim SS, Kato S, Sanders PD, Patel SP, Sanghvi P, et al. Exceptional response to nivolumab and stereotactic body radiation therapy (SBRT) in neuroendocrine cervical carcinoma with high tumor mutational burden: management considerations from the center for personalized cancer therapy at UC San Diego Moores Cancer Center. Oncologist. 2017;22(Suppl 6):631–7.

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Solinas C, Gombos A, Latifyan S, Piccart-Gebhart M, Kok M, Buisseret L. Targeting immune checkpoints in breast cancer: an update of early results. ESMO Open. 2017;2(Suppl 5):e000255.

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Lakhani SR, Jacquemier J, Sloane JP, Gusterson BA, Anderson TJ, van de Vijver MJ, et al. Multifactorial analysis of differences between sporadic breast cancers and cancers involving BRCA1 and BRCA2 mutations. JNCI: J Natl Cancer Inst. 1998;90(Suppl 15):1138–45.

    CAS  PubMed  Google Scholar 

  109. 109.

    Davoli T, Uno H, Wooten EC, Elledge SJ. Tumor aneuploidy correlates with markers of immuneevasion and with reduced response to immunotherapy. Science. 2017;355(Suppl 6322):eaaf8399.

  110. 110.

    Kraya AA, Maxwell KN, Wubbenhorst B, Wenz BM, Pluta J, Rech al. Genomic signatures predict the immunogenicity of BRCA-deficient breast cancer. Clin Cancer Res. 2019;0468:2018

    Google Scholar 

  111. 111.

    Wen WX, Leong C-O. Association of BRCA1- and BRCA2-deficiency with mutation burden, expression of PD-L1/PD-1, immune infiltrates, and T cell-inflamed signature in breast cancer. PLoS ONE. 2019;14(Suppl 4):e0215381.

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017;127(Suppl 8):2930–40.

    PubMed  PubMed Central  Google Scholar 

  113. 113.

    Dai Y, Sun C, Feng Y, Jia Q, Zhu B. Potent immunogenicity in BRCA1-mutated patients with highgradeserous ovarian carcinoma. J Cell Mol Med. 2018;22(Suppl 8):3979–86.

    CAS  PubMed Central  Google Scholar 

  114. 114.

    Strickland KC, Howitt BE, Shukla SA, Rodig S, Ritterhouse LL, Liu JF, et al. Association and prognostic significance of BRCA1/2-mutation status with neoantigen load, number of tumor-infiltrating lymphocytes and expression of PD-1/PD-L1 in high grade serous ovarian cancer. Oncotarget. 2016;7(Suppl 12):13587–98.

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Disis ML, Taylor MH, Kelly K, Beck JT, Gordon M, Moore KM, et al. Efficacy and safety of avelumab for patients with recurrent or refractory ovarian cancer: phase 1b results from the JAVELIN Solid Tumor Trial. JAMA Oncol. 2019;5(Suppl 3):393–401.

    PubMed  PubMed Central  Google Scholar 

  116. 116.

    Avelumab alone or in combination with pegylated liposomal doxorubicin versus pegylated liposomal doxorubicin alone in platinum-resistant or refractory epithelial ovarian cancer: Primary and biomarker analysis of the phase III JAVELIN Ovarian 200 trial. In Proc. 50th Annual Meeting of the Society of Gynecologic Oncology. Honolulu;2019.

  117. 117.

    Nishino M, Ramaiya NH, Hatabu H, Hodi FS. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat Rev Clin Oncol. 2017;14(Suppl 11):655–68.

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Tanoue T, Morita S, Plichta DR, Skelly AN, Suda W, Sugiura Y, et al. A defined commensal consortium elicits CD8 T cells and anti-cancer immunity. Nature. 2019;565(Suppl 7741):600–5.

    CAS  Google Scholar 

  119. 119.

    Vetizou M, Pitt JM, Daillere R, Lepage P, Waldschmitt N, Flament C, et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science. 2015;350(Suppl 6264):1079–84.

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Angelosanto JM, Blackburn SD, Crawford A, Wherry EJ. Progressive loss of memory T cell potential and commitment to exhaustion during chronic viral infection. J Virol. 2012;86(Suppl 15):8161–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Youngblood B, Oestreich Kenneth J, Ha S-J, Duraiswamy J, Akondy Rama S, West Erin E, et al. Chronic virus infection enforces demethylation of the locus that encodes PD-1 in antigen-specific CD8+ T cells. Immunity. 2011;35(Suppl 3):400–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Ghoneim HE, Fan Y, Moustaki A, Abdelsamed HA, Dash P, Dogra P, et al. De novo epigenetic programs inhibit Pd-1 blockade-mediated T Cell rejuvenation. Cell. 2017;170(Suppl 1):142–57.e19.

    CAS  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Chatterjee A, Rodger EJ, Ahn A, Stockwell PA, Parry M, Motwani J, et al. Marked global DNA hypomethylation is associated with constitutive PD-L1 expression in melanoma. iScience. 2018;4:312–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Liu M, Zhou J, Chen Z, Cheng ASL. Understanding the epigenetic regulation of tumours and their microenvironments: opportunities and problems for epigenetic therapy. J Pathol. 2016;241:10–24.

    PubMed  Google Scholar 

  125. 125.

    Myzak MC, Dashwood WM, Orner GA, Ho E, Dashwood RH. Sulforaphane inhibits histone deacetylase in vivo and suppresses tumorigenesis in Apcmin mice. FASEB J. 2006;20(Suppl 3):506–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    McCabe MT, Ott HM, Ganji G, Korenchuk S, Thompson C, Van Aller GS, et al. EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations. Nature. 2012;492(Suppl 7427):108–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 127.

    Briere D, Sudhakar N, Woods DM, Hallin J, Engstrom LD, Aranda R, et al. The class I/IV HDAC inhibitor mocetinostat increases tumor antigen presentation, decreases immune suppressive cell types and augments checkpoint inhibitor therapy. Cancer Immunol Immunother. 2018;67(Suppl 3):381–92.

    CAS  PubMed  Google Scholar 

  128. 128.

    Llopiz D, Ruiz M, Villanueva L, Iglesias T, Silva L, Egea J, et al. Enhanced anti-tumor efficacy of checkpoint inhibitors in combination with the histone deacetylase inhibitor Belinostat in a murine hepatocellular carcinoma model. Cancer Immunol, Immunother. 2019;68(Suppl 3):379–93.

    CAS  Google Scholar 

  129. 129.

    Conway JR, Kofman E, Mo SS, Elmarakeby H, Van Allen E. Genomics of response to immune checkpoint therapies for cancer: implications for precision medicine. Genome Med. 2018;10(Suppl 1):93.

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Ott PA, Bang YJ, Piha-Paul SA, Razak ARA, Bennouna J, Soria JC, et al. T-cell–inflamed gene-expression profile, programmed death ligand 1 expression, and tumor mutational burden predict efficacy in patients treated with pembrolizumab across 20 cancers: KEYNOTE-028. J Clin Oncol. 2019;37(Suppl 4):318–27.

    PubMed  Google Scholar 

  131. 131.

    Chae YK, Anker JF, Carneiro BA, Chandra S, Kaplan J, Kalyan A, et al. Genomic landscape of DNA repair genes in cancer. Oncotarget. 2016;7(Suppl 17):23312.

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Levine DA, Network CGAR. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497(Suppl 7447):67.

    PubMed  PubMed Central  Google Scholar 

  133. 133.

    Hussein YR, Weigelt B, Levine DA, Schoolmeester JK, Dao LN, Balzer BL, et al. Clinicopathological analysis of endometrial carcinomas harboring somatic POLE exonuclease domain mutations. Mod Pathol. 2015;28(Suppl 4):505.

    CAS  PubMed  Google Scholar 

  134. 134.

    Redig AJ, Jänne PA. Basket trials and the evolution of clinical trial design in an era of genomic medicine. J Clin Oncol. 2015;33(Suppl 9):975–7.

    CAS  PubMed  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to John Nemunaitis.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Choucair, K., Morand, S., Stanbery, L. et al. TMB: a promising immune-response biomarker, and potential spearhead in advancing targeted therapy trials. Cancer Gene Ther 27, 841–853 (2020).

Download citation

Further reading


Quick links