- Email: [email protected]
Seek and Destroy: Relating Cancer Drivers to Therapies
Cancer Cell
Previews response to Gln disruption, and they suggest novel patient stratification and treatment strategies in breast cancer and other malignancies.
Hensley, C.T., Wasti, A.T., and DeBerardinis, R.J. (2013). J. Clin. Invest. 123, 3678–3684.
REFERENCES
Li, J., Csibi, A., Yang, S., Hoffman, G.R., Li, C., Zhang, E., Yu, J.J., and Blenis, J. (2015). Proc. Natl. Acad. Sci. USA 112, E21–E29.
Doherty, J.R., and Cleveland, J.L. (2013). J. Clin. Invest. 123, 3685–3692. Gross, M.I., Demo, S.D., Dennison, J.B., Chen, L., Chernov-Rogan, T., Goyal, B., Janes, J.R., Laidig, G.J., Lewis, E.R., Li, J., et al. (2014). Mol. Cancer Ther. 13, 890–901.
Jeon, Y.J., Khelif, S., Ratnikov, B., Scott, D.A., Feng, Y., Parisi, F., Ruller, C., Lau, E., Kim, H., Brill, L.M., et al. (2015). Cancer Cell 27, this issue, 354–369.
Marcu, M.G., Doyle, M., Bertolotti, A., Ron, D., Hendershot, L., and Neckers, L. (2002). Mol. Cell. Biol. 22, 8506–8513. Nicklin, P., Bergman, P., Zhang, B., Triantafellow, E., Wang, H., Nyfeler, B., Yang, H., Hild, M.,
Kung, C., Wilson, C., et al. (2009). Cell 136, 521–534. Valencia, T., Kim, J.Y., Abu-Baker, S., MoscatPardos, J., Ahn, C.S., Reina-Campos, M., Duran, A., Castilla, E.A., Metallo, C.M., Diaz-Meco, M.T., and Moscat, J. (2014). Cancer Cell 26, 121–135. Venneti, S., Dunphy, M.P., Zhang, H., Pitter, K.L., Zanzonico, P., Campos, C., Carlin, S.D., La Rocca, G., Lyashenko, S., Ploessl, K., et al. (2015). Sci. Transl. Med. 7, 274ra217. Wang, M., and Kaufman, R.J. (2014). Nat. Rev. Cancer 14, 581–597.
Seek and Destroy: Relating Cancer Drivers to Therapies Emmanuel Martinez-Ledesma,1 John F. de Groot,2 and Roel G.W. Verhaak1,3,* 1Department
of Genomic Medicine of Neuro-Oncology 3Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.ccell.2015.02.011 2Department
In this issue of Cancer Cell, Rubio-Perez and colleagues present an in silico prescription strategy based on identifying somatic driver alterations and druggability options. Although relatively few patients were found treatable following current clinical guidelines, many more could benefit from drug repurposing, considering compounds at various stages of (pre-)clinical investigation. Precision oncology aims to provide clinical management tailored toward the molecular characteristics of the patient’s tumor (Garraway et al., 2013) and was defined as a priority by the US government through the recently announced Precision Medicine Initiative. Molecular characterization efforts such as The Cancer Genome Atlas (TCGA) have laid the foundation for precision oncology. At present, TCGA has analyzed over 11,000 tumors from 34 cancer types, which identified somatic and germ-line aberrations including single nucleotide variants, DNA copy number alterations, fusion genes, epigenetic modifications, and gene expression signatures. Now that the molecular landscape has been defined for many tumor types, a challenge remains to associate these alterations with current therapies in the appropriate clinical context. Agents targeting molecular defects are part of the standard
of care for subsets of tumors such as melanoma, lung cancer, and chronic myeloid leukemia, but the potential benefit of targeted therapies across cancer is unknown. In this issue of Cancer Cell, RubioPerez et al. (2015) propose a three-step in silico drug prescription strategy that connects patients to therapeutic regimens targeting the tumors’ genomic alterations (Figure 1). First, somatic alterations in genes predicted to drive tumorigenesis were detected in 6,792 tumor samples across 28 cancer types, which included 4,068 tumors from 16 TCGA studies. Part of the analysis included somatic point mutations and small insertions/deletions, focal DNA copy number alterations, and transcript fusions for TCGA samples, but they were limited to mutations only for non-TCGA cohorts. Using three statistical methods, genes under positive selection were identified, which resulted in a list
of 459 significantly altered genes. Second, all gene alterations were classified as loss of function or activating. Third, therapeutic agents targeting each driver gene directly, indirectly, or through gene therapy were collected. This included all Food and Drug Administration (FDA)approved drugs, therapies being evaluated in clinical trials, and compounds supported by preclinical data. Finally, drugs were matched with somatic alterations that were predicted to result in gene activation in order to link patients with a targeted therapy. Using this approach, Rubio-Perez et al. (2015) found that only 5.9% of the patients could potentially benefit from approved therapies following standard clinical guidelines; a similar percentage of oncology patients are treated on therapeutic clinical trials. However, this initial result did not consider the many clinical trials designed to evaluate targeted
Cancer Cell 27, March 9, 2015 ª2015 Elsevier Inc. 319
Cancer Cell
Previews
CNA
Figure 1. Strategy of Personalized In Silico Prescription of Anticancer Drugs
therapies in cancer that are currently ongoing. Importantly, by considering repurposing of FDA-approved drugs (Patel et al., 2013), including therapy indications from other tumor types but consistent with the genomic dependency or demonstrated off-target effects associated with the drug, the percentage of patients predicted to benefit from targeted agents increased to 40.2%. This percentage rose to an optimistic 73.3% when drugs currently under clinical trials were included, where it should be recognized that only 10% of oncology drugs under investigation are ultimately FDA-approved. The contributions from this study are 2-fold: first, the paper demonstrates the potential of targeted therapy on a large and relatively unbiased cohort of patients across many different tumor types, and second, it provides repurposing strategies for existing drugs that could potentially increase the percentage of patients for which targeted therapies might be appropriate. The in silico drug prescription strategy relied heavily on drug repurposing (Patel et al., 2013), an approach that requires critical evaluation. Repurposing opportunities were clustered into three tiers. (1) Disease repurposing, which occurs when the drug targets the driver gene but is prescribed for a non-cancer disease or another cancer type (20.5%).
(2) Strong off-target repurposing when agents show stronger affinity for a gene other than the target gene; indirect target repurposing, which targets genes functionally regulating the primary target; and alterations of the target gene different from the ones for which the drug was designed, e.g., using the drug to target a mutation instead of an amplification (14.4%). (3) Drug off-target repurposing, which refers to the interaction between a drug and driver gene different from the target gene, but with an affinity greater than 1 mM (2.7%). An example of the potential of drug repurposing is the coupling of tumors carrying activating alterations of the receptor tyrosine kinase MET to drugs such as crizotinib, for which incidental evidence has been reported (Chi et al., 2012). There are clear limitations to this strategy, including the need for clinical validation. For example, repurposing strategies report 154 glioblastomas (40.6% of the GBM cohort) with amplifications of receptor tyrosine kinase EGFR, which are predicted to respond to dasatinib and lapatinib, while this strategy notoriously failed to produce significant outcome benefits in clinical trials (van den Bent et al., 2009). This poses important questions on the validity of drug repurposing and whether mutations in genes that are targetable should be treated when alter-
320 Cancer Cell 27, March 9, 2015 ª2015 Elsevier Inc.
ations in the respective gene have not been found to be significant in that particular tumor type. The basket trial concept in which the presence of a molecular response marker has priority over tumor histology in determining optimal care is currently being widely used to verify clinical utility of inhibiting a specific target (Redig and Ja¨nne, 2015). The approach taken by Rubio-Perez et al. (2015) relied on computational analysis methods that are subject to ongoing optimization. The overlap between the 459 driver gene set with previously reported pan-cancer drive gene sets ranged from 40% to 75% (Gonzalez-Perez et al., 2013; Kandoth et al., 2013; Lawrence et al., 2014), suggesting that the driver gene set may vary dependent on the tumor cohort analyzed. Therapies designed against genes that were not on the list of 459 driver genes were not considered for therapeutic targeting, and this can be questioned, particularly in the context of basket trials focused on rare driver events such as transcript fusions. A known confounding factor is the lack of overlap between mutation caller methods, which is estimated at 50%–90% depending on tumor type, method, and coverage (Cibulskis et al., 2013). In a clinical setting, the sequence coverage is generally much higher than when performing exome-wide sequencing, which may address the mutation calling heterogeneity issue. This type of study corroborates the importance of initiatives such as TCGA and the International Cancer Genome Consortium (ICGC), where hundreds of researchers have collaborated extensively to generate molecular profiles from thousands of tumors and to provide preprocessed data sets that enable studies such as those by Rubio-Perez et al. (2015) and recently, a study on genomic biomarkers and clinical targetability in solid tumors (Dienstmann et al., 2015). If validated, the results from these studies suggest that drug repurposing may provide a valuable alternative in the absence of other treatment choices. Taken together, they provide landmark efforts that illustrate the potential of precision oncology. The small percentage of patients that currently benefit from molecularly targeted approaches commands expedition of drug development and the drug approval process, but the
Cancer Cell
Previews larger number of patients whose tumors harbor potentially druggable alterations sparks hope for a more positive outlook on patient outcomes in the near future. ACKNOWLEDGMENTS The work from the authors was supported by grant numbers U24 CA143883, P50 CA127001, P01 CA085878, and R01 CA190121 from the United States NIH/National Cancer Institute and Cancer Prevention & Research Institute of Texas (CPRIT) grant number R140606. E.M. is funded in part by a CONACYT fellowship. REFERENCES Chi, A.S., Batchelor, T.T., Kwak, E.L., Clark, J.W., Wang, D.L., Wilner, K.D., Louis, D.N., and Iafrate, A.J. (2012). J. Clin. Oncol. 30, e30–e33.
Cibulskis, K., Lawrence, M.S., Carter, S.L., Sivachenko, A., Jaffe, D., Sougnez, C., Gabriel, S., Meyerson, M., Lander, E.S., and Getz, G. (2013). Nat. Biotechnol. 31, 213–219. Dienstmann, R., Jang, I.S., Bot, B., Friend, S., and Guinney, J. (2015). Cancer Discov 5, 118–123. Garraway, L.A., Verweij, J., and Ballman, K.V. (2013). J. Clin. Oncol. 31, 1803–1805. Gonzalez-Perez, A., Perez-Llamas, C., Deu-Pons, J., Tamborero, D., Schroeder, M.P., Jene-Sanz, A., Santos, A., and Lopez-Bigas, N. (2013). Nat. Methods 10, 1081–1082. Kandoth, C., McLellan, M.D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J.F., Wyczalkowski, M.A., et al. (2013). Nature 502, 333–339. Lawrence, M.S., Stojanov, P., Mermel, C.H., Robinson, J.T., Garraway, L.A., Golub, T.R., Meyerson,
M., Gabriel, S.B., Lander, E.S., and Getz, G. (2014). Nature 505, 495–501. Patel, M.N., Halling-Brown, M.D., Tym, J.E., Workman, P., and Al-Lazikani, B. (2013). Nat. Rev. Drug Discov. 12, 35–50. Redig, A.J., and Ja¨nne, P.A. (2015). J. Clin. Oncol. Published online February 9, 2015. http://dx.doi. org/10.1200/JCO.2014.59.8433. Rubio-Perez, C., Tamborero, D., Schroeder, M.P., Antolı´n, A.A., Deu-Pons, J., Perez-Llamas, C., Mestres, J., Gonzalez-Perez, A., and LopezBigas, N. (2015). Cancer Cell 27, this issue, 382–396. van den Bent, M.J., Brandes, A.A., Rampling, R., Kouwenhoven, M.C., Kros, J.M., Carpentier, A.F., Clement, P.M., Frenay, M., Campone, M., Baurain, J.F., et al. (2009). J. Clin. Oncol. 27, 1268–1274.
The Pre-BCR to the Rescue: Therapeutic Targeting of Pre-B Cell ALL Thomas Trimarchi1 and Iannis Aifantis1,* 1Department of Pathology and Howard Hughes Medical Institute, NYU School of Medicine New York, NY 10016, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.ccell.2015.02.012
Pre B-ALL is an aggressive cancer of the blood for which treatment of patients with relapsed and refractory disease remains a challenge. In this issue of Cancer Cell, Geng and colleagues surveyed the activation status of the pre-B cell receptor and comprehensively investigated downstream signaling mechanisms currently targetable with small molecule inhibitors.
Pre-B cell acute lymphoblastic leukemia (pre B-ALL) is an aggressive hematological malignancy that is the result of oncogenic transformation of an early B-lymphocyte progenitor. It is the most common form of pediatric cancer. Despite tremendous advances in the treatment of this disease over the last decades, including recent breakthroughs in the area of immunotherapy (Grupp et al., 2013), there remain patients who experience relapsed disease for which the prognosis is dismal (Bhatla et al., 2014). Therefore, development of therapeutic strategies that target the specific mechanisms that contribute to the oncogenic state are of great interest, because these may offer options for those who are untreatable with current protocols
and possibly a more effective alternative even for those who do respond to standard therapy. In this issue of Cancer Cell, Geng et al. (2015) have taken an important key step toward that goal. Building on previous studies that clearly demonstrated the importance of signaling through the preB cell receptor (pre-BCR) for B-lymphocyte survival during normal development (Hess et al., 2001), the authors aimed to clarify if a similar mode of signaling might represent a targetable susceptibility in pre-B-ALL (Geng et al., 2015). There is precedent for this hypothesis, because studies in more phenotypically mature B cell malignancies, such as chronic lymphocytic leukemia, diffuse large B cell lymphoma, and mantle cell lymphoma, re-
vealed a critical role for B cell receptor (BCR) signaling in disease pathogenesis (Byrd et al., 2013; Davis et al., 2010; Wang et al., 2013). These studies have described two distinct modes of BCR signaling. Tonic BCR signaling has been shown to be important for the survival of normal B cells as well as tumor cells and acts primarily through SRC and SYK kinases (Juszczynski et al., 2009), while chronic-activated BCR signaling engages multiple downstream signaling cascades, including NF-kB and BTK (Davis et al., 2010). This information has led to significant translational breakthroughs, because inhibitors of both of these signaling mechanisms have been developed (Young and Staudt, 2013). Therefore, the realization of a similar route of
Cancer Cell 27, March 9, 2015 ª2015 Elsevier Inc. 321
Previews response to Gln disruption, and they suggest novel patient stratification and treatment strategies in breast cancer and other malignancies.
Hensley, C.T., Wasti, A.T., and DeBerardinis, R.J. (2013). J. Clin. Invest. 123, 3678–3684.
REFERENCES
Li, J., Csibi, A., Yang, S., Hoffman, G.R., Li, C., Zhang, E., Yu, J.J., and Blenis, J. (2015). Proc. Natl. Acad. Sci. USA 112, E21–E29.
Doherty, J.R., and Cleveland, J.L. (2013). J. Clin. Invest. 123, 3685–3692. Gross, M.I., Demo, S.D., Dennison, J.B., Chen, L., Chernov-Rogan, T., Goyal, B., Janes, J.R., Laidig, G.J., Lewis, E.R., Li, J., et al. (2014). Mol. Cancer Ther. 13, 890–901.
Jeon, Y.J., Khelif, S., Ratnikov, B., Scott, D.A., Feng, Y., Parisi, F., Ruller, C., Lau, E., Kim, H., Brill, L.M., et al. (2015). Cancer Cell 27, this issue, 354–369.
Marcu, M.G., Doyle, M., Bertolotti, A., Ron, D., Hendershot, L., and Neckers, L. (2002). Mol. Cell. Biol. 22, 8506–8513. Nicklin, P., Bergman, P., Zhang, B., Triantafellow, E., Wang, H., Nyfeler, B., Yang, H., Hild, M.,
Kung, C., Wilson, C., et al. (2009). Cell 136, 521–534. Valencia, T., Kim, J.Y., Abu-Baker, S., MoscatPardos, J., Ahn, C.S., Reina-Campos, M., Duran, A., Castilla, E.A., Metallo, C.M., Diaz-Meco, M.T., and Moscat, J. (2014). Cancer Cell 26, 121–135. Venneti, S., Dunphy, M.P., Zhang, H., Pitter, K.L., Zanzonico, P., Campos, C., Carlin, S.D., La Rocca, G., Lyashenko, S., Ploessl, K., et al. (2015). Sci. Transl. Med. 7, 274ra217. Wang, M., and Kaufman, R.J. (2014). Nat. Rev. Cancer 14, 581–597.
Seek and Destroy: Relating Cancer Drivers to Therapies Emmanuel Martinez-Ledesma,1 John F. de Groot,2 and Roel G.W. Verhaak1,3,* 1Department
of Genomic Medicine of Neuro-Oncology 3Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.ccell.2015.02.011 2Department
In this issue of Cancer Cell, Rubio-Perez and colleagues present an in silico prescription strategy based on identifying somatic driver alterations and druggability options. Although relatively few patients were found treatable following current clinical guidelines, many more could benefit from drug repurposing, considering compounds at various stages of (pre-)clinical investigation. Precision oncology aims to provide clinical management tailored toward the molecular characteristics of the patient’s tumor (Garraway et al., 2013) and was defined as a priority by the US government through the recently announced Precision Medicine Initiative. Molecular characterization efforts such as The Cancer Genome Atlas (TCGA) have laid the foundation for precision oncology. At present, TCGA has analyzed over 11,000 tumors from 34 cancer types, which identified somatic and germ-line aberrations including single nucleotide variants, DNA copy number alterations, fusion genes, epigenetic modifications, and gene expression signatures. Now that the molecular landscape has been defined for many tumor types, a challenge remains to associate these alterations with current therapies in the appropriate clinical context. Agents targeting molecular defects are part of the standard
of care for subsets of tumors such as melanoma, lung cancer, and chronic myeloid leukemia, but the potential benefit of targeted therapies across cancer is unknown. In this issue of Cancer Cell, RubioPerez et al. (2015) propose a three-step in silico drug prescription strategy that connects patients to therapeutic regimens targeting the tumors’ genomic alterations (Figure 1). First, somatic alterations in genes predicted to drive tumorigenesis were detected in 6,792 tumor samples across 28 cancer types, which included 4,068 tumors from 16 TCGA studies. Part of the analysis included somatic point mutations and small insertions/deletions, focal DNA copy number alterations, and transcript fusions for TCGA samples, but they were limited to mutations only for non-TCGA cohorts. Using three statistical methods, genes under positive selection were identified, which resulted in a list
of 459 significantly altered genes. Second, all gene alterations were classified as loss of function or activating. Third, therapeutic agents targeting each driver gene directly, indirectly, or through gene therapy were collected. This included all Food and Drug Administration (FDA)approved drugs, therapies being evaluated in clinical trials, and compounds supported by preclinical data. Finally, drugs were matched with somatic alterations that were predicted to result in gene activation in order to link patients with a targeted therapy. Using this approach, Rubio-Perez et al. (2015) found that only 5.9% of the patients could potentially benefit from approved therapies following standard clinical guidelines; a similar percentage of oncology patients are treated on therapeutic clinical trials. However, this initial result did not consider the many clinical trials designed to evaluate targeted
Cancer Cell 27, March 9, 2015 ª2015 Elsevier Inc. 319
Cancer Cell
Previews
CNA
Figure 1. Strategy of Personalized In Silico Prescription of Anticancer Drugs
therapies in cancer that are currently ongoing. Importantly, by considering repurposing of FDA-approved drugs (Patel et al., 2013), including therapy indications from other tumor types but consistent with the genomic dependency or demonstrated off-target effects associated with the drug, the percentage of patients predicted to benefit from targeted agents increased to 40.2%. This percentage rose to an optimistic 73.3% when drugs currently under clinical trials were included, where it should be recognized that only 10% of oncology drugs under investigation are ultimately FDA-approved. The contributions from this study are 2-fold: first, the paper demonstrates the potential of targeted therapy on a large and relatively unbiased cohort of patients across many different tumor types, and second, it provides repurposing strategies for existing drugs that could potentially increase the percentage of patients for which targeted therapies might be appropriate. The in silico drug prescription strategy relied heavily on drug repurposing (Patel et al., 2013), an approach that requires critical evaluation. Repurposing opportunities were clustered into three tiers. (1) Disease repurposing, which occurs when the drug targets the driver gene but is prescribed for a non-cancer disease or another cancer type (20.5%).
(2) Strong off-target repurposing when agents show stronger affinity for a gene other than the target gene; indirect target repurposing, which targets genes functionally regulating the primary target; and alterations of the target gene different from the ones for which the drug was designed, e.g., using the drug to target a mutation instead of an amplification (14.4%). (3) Drug off-target repurposing, which refers to the interaction between a drug and driver gene different from the target gene, but with an affinity greater than 1 mM (2.7%). An example of the potential of drug repurposing is the coupling of tumors carrying activating alterations of the receptor tyrosine kinase MET to drugs such as crizotinib, for which incidental evidence has been reported (Chi et al., 2012). There are clear limitations to this strategy, including the need for clinical validation. For example, repurposing strategies report 154 glioblastomas (40.6% of the GBM cohort) with amplifications of receptor tyrosine kinase EGFR, which are predicted to respond to dasatinib and lapatinib, while this strategy notoriously failed to produce significant outcome benefits in clinical trials (van den Bent et al., 2009). This poses important questions on the validity of drug repurposing and whether mutations in genes that are targetable should be treated when alter-
320 Cancer Cell 27, March 9, 2015 ª2015 Elsevier Inc.
ations in the respective gene have not been found to be significant in that particular tumor type. The basket trial concept in which the presence of a molecular response marker has priority over tumor histology in determining optimal care is currently being widely used to verify clinical utility of inhibiting a specific target (Redig and Ja¨nne, 2015). The approach taken by Rubio-Perez et al. (2015) relied on computational analysis methods that are subject to ongoing optimization. The overlap between the 459 driver gene set with previously reported pan-cancer drive gene sets ranged from 40% to 75% (Gonzalez-Perez et al., 2013; Kandoth et al., 2013; Lawrence et al., 2014), suggesting that the driver gene set may vary dependent on the tumor cohort analyzed. Therapies designed against genes that were not on the list of 459 driver genes were not considered for therapeutic targeting, and this can be questioned, particularly in the context of basket trials focused on rare driver events such as transcript fusions. A known confounding factor is the lack of overlap between mutation caller methods, which is estimated at 50%–90% depending on tumor type, method, and coverage (Cibulskis et al., 2013). In a clinical setting, the sequence coverage is generally much higher than when performing exome-wide sequencing, which may address the mutation calling heterogeneity issue. This type of study corroborates the importance of initiatives such as TCGA and the International Cancer Genome Consortium (ICGC), where hundreds of researchers have collaborated extensively to generate molecular profiles from thousands of tumors and to provide preprocessed data sets that enable studies such as those by Rubio-Perez et al. (2015) and recently, a study on genomic biomarkers and clinical targetability in solid tumors (Dienstmann et al., 2015). If validated, the results from these studies suggest that drug repurposing may provide a valuable alternative in the absence of other treatment choices. Taken together, they provide landmark efforts that illustrate the potential of precision oncology. The small percentage of patients that currently benefit from molecularly targeted approaches commands expedition of drug development and the drug approval process, but the
Cancer Cell
Previews larger number of patients whose tumors harbor potentially druggable alterations sparks hope for a more positive outlook on patient outcomes in the near future. ACKNOWLEDGMENTS The work from the authors was supported by grant numbers U24 CA143883, P50 CA127001, P01 CA085878, and R01 CA190121 from the United States NIH/National Cancer Institute and Cancer Prevention & Research Institute of Texas (CPRIT) grant number R140606. E.M. is funded in part by a CONACYT fellowship. REFERENCES Chi, A.S., Batchelor, T.T., Kwak, E.L., Clark, J.W., Wang, D.L., Wilner, K.D., Louis, D.N., and Iafrate, A.J. (2012). J. Clin. Oncol. 30, e30–e33.
Cibulskis, K., Lawrence, M.S., Carter, S.L., Sivachenko, A., Jaffe, D., Sougnez, C., Gabriel, S., Meyerson, M., Lander, E.S., and Getz, G. (2013). Nat. Biotechnol. 31, 213–219. Dienstmann, R., Jang, I.S., Bot, B., Friend, S., and Guinney, J. (2015). Cancer Discov 5, 118–123. Garraway, L.A., Verweij, J., and Ballman, K.V. (2013). J. Clin. Oncol. 31, 1803–1805. Gonzalez-Perez, A., Perez-Llamas, C., Deu-Pons, J., Tamborero, D., Schroeder, M.P., Jene-Sanz, A., Santos, A., and Lopez-Bigas, N. (2013). Nat. Methods 10, 1081–1082. Kandoth, C., McLellan, M.D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J.F., Wyczalkowski, M.A., et al. (2013). Nature 502, 333–339. Lawrence, M.S., Stojanov, P., Mermel, C.H., Robinson, J.T., Garraway, L.A., Golub, T.R., Meyerson,
M., Gabriel, S.B., Lander, E.S., and Getz, G. (2014). Nature 505, 495–501. Patel, M.N., Halling-Brown, M.D., Tym, J.E., Workman, P., and Al-Lazikani, B. (2013). Nat. Rev. Drug Discov. 12, 35–50. Redig, A.J., and Ja¨nne, P.A. (2015). J. Clin. Oncol. Published online February 9, 2015. http://dx.doi. org/10.1200/JCO.2014.59.8433. Rubio-Perez, C., Tamborero, D., Schroeder, M.P., Antolı´n, A.A., Deu-Pons, J., Perez-Llamas, C., Mestres, J., Gonzalez-Perez, A., and LopezBigas, N. (2015). Cancer Cell 27, this issue, 382–396. van den Bent, M.J., Brandes, A.A., Rampling, R., Kouwenhoven, M.C., Kros, J.M., Carpentier, A.F., Clement, P.M., Frenay, M., Campone, M., Baurain, J.F., et al. (2009). J. Clin. Oncol. 27, 1268–1274.
The Pre-BCR to the Rescue: Therapeutic Targeting of Pre-B Cell ALL Thomas Trimarchi1 and Iannis Aifantis1,* 1Department of Pathology and Howard Hughes Medical Institute, NYU School of Medicine New York, NY 10016, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.ccell.2015.02.012
Pre B-ALL is an aggressive cancer of the blood for which treatment of patients with relapsed and refractory disease remains a challenge. In this issue of Cancer Cell, Geng and colleagues surveyed the activation status of the pre-B cell receptor and comprehensively investigated downstream signaling mechanisms currently targetable with small molecule inhibitors.
Pre-B cell acute lymphoblastic leukemia (pre B-ALL) is an aggressive hematological malignancy that is the result of oncogenic transformation of an early B-lymphocyte progenitor. It is the most common form of pediatric cancer. Despite tremendous advances in the treatment of this disease over the last decades, including recent breakthroughs in the area of immunotherapy (Grupp et al., 2013), there remain patients who experience relapsed disease for which the prognosis is dismal (Bhatla et al., 2014). Therefore, development of therapeutic strategies that target the specific mechanisms that contribute to the oncogenic state are of great interest, because these may offer options for those who are untreatable with current protocols
and possibly a more effective alternative even for those who do respond to standard therapy. In this issue of Cancer Cell, Geng et al. (2015) have taken an important key step toward that goal. Building on previous studies that clearly demonstrated the importance of signaling through the preB cell receptor (pre-BCR) for B-lymphocyte survival during normal development (Hess et al., 2001), the authors aimed to clarify if a similar mode of signaling might represent a targetable susceptibility in pre-B-ALL (Geng et al., 2015). There is precedent for this hypothesis, because studies in more phenotypically mature B cell malignancies, such as chronic lymphocytic leukemia, diffuse large B cell lymphoma, and mantle cell lymphoma, re-
vealed a critical role for B cell receptor (BCR) signaling in disease pathogenesis (Byrd et al., 2013; Davis et al., 2010; Wang et al., 2013). These studies have described two distinct modes of BCR signaling. Tonic BCR signaling has been shown to be important for the survival of normal B cells as well as tumor cells and acts primarily through SRC and SYK kinases (Juszczynski et al., 2009), while chronic-activated BCR signaling engages multiple downstream signaling cascades, including NF-kB and BTK (Davis et al., 2010). This information has led to significant translational breakthroughs, because inhibitors of both of these signaling mechanisms have been developed (Young and Staudt, 2013). Therefore, the realization of a similar route of
Cancer Cell 27, March 9, 2015 ª2015 Elsevier Inc. 321